{smcl} {com}{sf}{ul off}{txt}{.-} name: {res} {txt}log: {res}C:\Thesis Research - Final\Thesis\Chapter2\Data_analysis\switch.smcl {txt}log type: {res}smcl {txt}opened on: {res} 7 Sep 2017, 09:48:11 {txt} {com}. *=============================================== . *SWITCHING REGRESSION MODEL . *=============================================== . gen fcba=fc //baseline fc {txt} {com}. gen diba=di //baseline di->distance from frontier measure {txt}(23 missing values generated) {com}. tab year, gen(yr) {txt}Year {c |} Freq. Percent Cum. {hline 12}{c +}{hline 35} 1999 {c |}{res} 10 4.33 4.33 {txt} 2000 {c |}{res} 13 5.63 9.96 {txt} 2001 {c |}{res} 13 5.63 15.58 {txt} 2002 {c |}{res} 13 5.63 21.21 {txt} 2003 {c |}{res} 13 5.63 26.84 {txt} 2004 {c |}{res} 13 5.63 32.47 {txt} 2005 {c |}{res} 13 5.63 38.10 {txt} 2006 {c |}{res} 13 5.63 43.72 {txt} 2007 {c |}{res} 13 5.63 49.35 {txt} 2008 {c |}{res} 13 5.63 54.98 {txt} 2009 {c |}{res} 13 5.63 60.61 {txt} 2010 {c |}{res} 13 5.63 66.23 {txt} 2011 {c |}{res} 13 5.63 71.86 {txt} 2012 {c |}{res} 13 5.63 77.49 {txt} 2013 {c |}{res} 13 5.63 83.12 {txt} 2014 {c |}{res} 13 5.63 88.74 {txt} 2015 {c |}{res} 13 5.63 94.37 {txt} 2016 {c |}{res} 13 5.63 100.00 {txt}{hline 12}{c +}{hline 35} Total {c |}{res} 231 100.00 {txt} {com}. tab Firms, gen(f) {txt}Firms {c |} Freq. Percent Cum. {hline 25}{c +}{hline 35} ARM {c |}{res} 18 7.79 7.79 {txt} BOC {c |}{res} 18 7.79 15.58 {txt} Bamburi Cement {c |}{res} 18 7.79 23.38 {txt}British American Tobacco {c |}{res} 18 7.79 31.17 {txt} Carbacid {c |}{res} 18 7.79 38.96 {txt} Crown Berger {c |}{res} 18 7.79 46.75 {txt} EA Cable {c |}{res} 18 7.79 54.55 {txt} EA Portland Cement {c |}{res} 17 7.36 61.90 {txt} EABL {c |}{res} 17 7.36 69.26 {txt} Eveready {c |}{res} 18 7.79 77.06 {txt} Mumias {c |}{res} 18 7.79 84.85 {txt} Sameer {c |}{res} 18 7.79 92.64 {txt} Unga {c |}{res} 17 7.36 100.00 {txt}{hline 25}{c +}{hline 35} Total {c |}{res} 231 100.00 {txt} {com}. *gen q_t=((finpsn37*adnotes31/adnotes22)+finpsn1-(finpsn37+finpsn51))/finpsn1 . *gen q=l.q_t //to measure at the beginning of the year as per the literature . *definition of equations . global fe "yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10 yr11 yr12 yr13 yr14 yr15 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13" {txt} {com}. eq poh: delta_D fdeficit sales profitab tangi mtb $fe {txt} {com}. eq inv: invrate q cfs $fe {txt} {com}. eq hpf: fc age size size2 $fe {txt} {com}. eq kzf: fc kz_cfs mtb kz_d kz_div kz_cs $fe {txt} {com}. eq wwf: fc ww_d ww_div ww_gsales ww_size ww_cs ww_cfs $fe {txt} {com}. eq hpd: di age size size2 $fe {txt} {com}. eq kzd: di kz_cfs mtb kz_d kz_div kz_cs $fe {txt} {com}. eq wwd: di ww_d ww_div ww_gsales ww_size ww_cs ww_cfs $fe {txt} {com}. *implementation of equations . local regime hpf kzf wwf {txt} {com}. local main poh inv {txt} {com}. foreach l of local regime {c -(} {txt} 2{com}. switchr poh `l' {txt} 3{com}. gen byte poh`l'=fc>0.5 //storing classification results in binary {txt} 4{com}. replace fc=fcba //reseting the initial values {txt} 5{com}. {c )-} {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 54}{space 4}-.4009293{col 67}{space 3} .3813382 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2167708{col 26}{space 2} .1976057{col 37}{space 1} 1.10{col 46}{space 3}0.274{col 54}{space 4}-.1730119{col 67}{space 3} .6065536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1537051{col 26}{space 2} .1971893{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.5426666{col 67}{space 3} .2352564 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0596839{col 26}{space 2} .1954351{col 37}{space 1} 0.31{col 46}{space 3}0.760{col 54}{space 4}-.3258174{col 67}{space 3} .4451852 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0358223{col 26}{space 2} .1980381{col 37}{space 1} 0.18{col 46}{space 3}0.857{col 54}{space 4}-.3548133{col 67}{space 3} .426458 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2612704{col 26}{space 2} .1923846{col 37}{space 1} 1.36{col 46}{space 3}0.176{col 54}{space 4}-.1182137{col 67}{space 3} .6407544 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .172893{col 26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00057 Average of the probability vector is: {txt}0.756 On iteration {com}50{txt} greatest diff is: {com}0.013758 on f8 in the{txt} second main {com}eqn {txt}Log-likelihood is : 128.64315 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00018 Average of the probability vector is: {txt}0.758 On iteration {com}100{txt} greatest diff is: {com}-0.001978 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 130.49443 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.759 On iteration {com}150{txt} greatest diff is: {com}-0.000924 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.12888 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.760 On iteration {com}200{txt} greatest diff is: {com}-0.000541 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.48283 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.760 On iteration {com}250{txt} greatest diff is: {com}-0.000399 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.67453 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.761 On iteration {com}300{txt} greatest diff is: {com}-0.000282 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.78713 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.761 On iteration {com}350{txt} greatest diff is: {com}-0.000218 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.85634 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.761 On iteration {com}400{txt} greatest diff is: {com}-0.000190 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.89942 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.761 On iteration {com}450{txt} greatest diff is: {com}-0.000174 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.92966 {res}This iteration took {com}1{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.761 On iteration {com}500{txt} greatest diff is: {com}-0.000168 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.94971 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.761 On iteration {com}550{txt} greatest diff is: {com}-0.000151 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.96373 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.761 On iteration {com}600{txt} greatest diff is: {com}-0.000135 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.97398 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.761 On iteration {com}650{txt} greatest diff is: {com}-0.000122 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.98173 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.761 On iteration {com}700{txt} greatest diff is: {com}-0.000110 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.98778 {res}This iteration took {com}0{res} second. On iter 750 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.761 On iteration {com}750{txt} greatest diff is: {com}-0.000101 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 131.9926 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 2.05e-15 1.33e-20 {txt} 5% {res} 3.24e-06 5.05e-16 {txt}10% {res} .0005214 2.05e-15 {txt}Obs {res} 215 {txt}25% {res} .5586432 2.70e-14 {txt}Sum of Wgt. {res} 215 {txt}50% {res} .9988902 {txt}Mean {res} .7613365 {txt}Largest Std. Dev. {res} .3763715 {txt}75% {res} 1 1 {txt}90% {res} 1 1 {txt}Variance {res} .1416555 {txt}95% {res} 1 1 {txt}Skewness {res}-1.211495 {txt}99% {res} 1 1 {txt}Kurtosis {res} 2.73786 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 200 {txt}{hline 13}{c +}{hline 34} F(29, 170) = {res} 684.98 {txt} Model {c |} {res} 2741.8033 29 94.5449414 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 23.4643994 170 .138025879 {txt}R-squared ={res} 0.9915 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9901 {txt} Total {c |} {res} 2765.2677 199 13.8958176 {txt}Root MSE = {res} .37152 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 9}age {c |}{col 14}{res}{space 2}-1.606206{col 26}{space 2} .10118{col 37}{space 1} -15.87{col 46}{space 3}0.000{col 54}{space 4}-1.805937{col 67}{space 3}-1.406475 {txt}{space 8}size {c |}{col 14}{res}{space 2} 4.27675{col 26}{space 2} .2560982{col 37}{space 1} 16.70{col 46}{space 3}0.000{col 54}{space 4} 3.771208{col 67}{space 3} 4.782292 {txt}{space 7}size2 {c |}{col 14}{res}{space 2}-.1309036{col 26}{space 2} .0098275{col 37}{space 1} -13.32{col 46}{space 3}0.000{col 54}{space 4}-.1503033{col 67}{space 3} -.111504 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-1.446287{col 26}{space 2} .1434833{col 37}{space 1} -10.08{col 46}{space 3}0.000{col 54}{space 4}-1.729525{col 67}{space 3}-1.163048 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-1.328803{col 26}{space 2} .1411336{col 37}{space 1} -9.42{col 46}{space 3}0.000{col 54}{space 4}-1.607403{col 67}{space 3}-1.050203 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-2.403067{col 26}{space 2} .1345981{col 37}{space 1} -17.85{col 46}{space 3}0.000{col 54}{space 4}-2.668766{col 67}{space 3}-2.137368 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.6008335{col 26}{space 2} .1375466{col 37}{space 1} -4.37{col 46}{space 3}0.000{col 54}{space 4}-.8723529{col 67}{space 3}-.3293142 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} 4.073986{col 26}{space 2} .161547{col 37}{space 1} 25.22{col 46}{space 3}0.000{col 54}{space 4} 3.755089{col 67}{space 3} 4.392882 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .927092{col 26}{space 2} .1268399{col 37}{space 1} 7.31{col 46}{space 3}0.000{col 54}{space 4} .6767079{col 67}{space 3} 1.177476 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} 7.359756{col 26}{space 2} .2279102{col 37}{space 1} 32.29{col 46}{space 3}0.000{col 54}{space 4} 6.909858{col 67}{space 3} 7.809655 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} 1.170959{col 26}{space 2} .121479{col 37}{space 1} 9.64{col 46}{space 3}0.000{col 54}{space 4} .9311576{col 67}{space 3} 1.410761 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} 1.207797{col 26}{space 2} .1203679{col 37}{space 1} 10.03{col 46}{space 3}0.000{col 54}{space 4} .9701887{col 67}{space 3} 1.445405 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-2.537992{col 26}{space 2} .1189972{col 37}{space 1} -21.33{col 46}{space 3}0.000{col 54}{space 4}-2.772894{col 67}{space 3}-2.303089 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-2.911009{col 26}{space 2} .1182801{col 37}{space 1} -24.61{col 46}{space 3}0.000{col 54}{space 4}-3.144496{col 67}{space 3}-2.677522 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.3053346{col 26}{space 2} .1183037{col 37}{space 1} -2.58{col 46}{space 3}0.011{col 54}{space 4}-.5388681{col 67}{space 3}-.0718012 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-2.170954{col 26}{space 2} .1177558{col 37}{space 1} -18.44{col 46}{space 3}0.000{col 54}{space 4}-2.403406{col 67}{space 3}-1.938502 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-1.880217{col 26}{space 2} .117816{col 37}{space 1} -15.96{col 46}{space 3}0.000{col 54}{space 4}-2.112787{col 67}{space 3}-1.647646 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 11.25729{col 26}{space 2} .2192288{col 37}{space 1} 51.35{col 46}{space 3}0.000{col 54}{space 4} 10.82452{col 67}{space 3} 11.69005 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 5.008204{col 26}{space 2} .1985393{col 37}{space 1} 25.23{col 46}{space 3}0.000{col 54}{space 4} 4.616284{col 67}{space 3} 5.400124 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 5.080117{col 26}{space 2} .190705{col 37}{space 1} 26.64{col 46}{space 3}0.000{col 54}{space 4} 4.703662{col 67}{space 3} 5.456571 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 11.51807{col 26}{space 2} .2260016{col 37}{space 1} 50.96{col 46}{space 3}0.000{col 54}{space 4} 11.07194{col 67}{space 3} 11.9642 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} 2.810251{col 26}{space 2} .168026{col 37}{space 1} 16.73{col 46}{space 3}0.000{col 54}{space 4} 2.478565{col 67}{space 3} 3.141937 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} -.276824{col 26}{space 2} .2064642{col 37}{space 1} -1.34{col 46}{space 3}0.182{col 54}{space 4}-.6843879{col 67}{space 3} .1307398 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 8.549694{col 26}{space 2} .1983941{col 37}{space 1} 43.09{col 46}{space 3}0.000{col 54}{space 4} 8.158061{col 67}{space 3} 8.941327 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 4.183641{col 26}{space 2} .2025821{col 37}{space 1} 20.65{col 46}{space 3}0.000{col 54}{space 4} 3.783741{col 67}{space 3} 4.583542 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 6.449228{col 26}{space 2} .237619{col 37}{space 1} 27.14{col 46}{space 3}0.000{col 54}{space 4} 5.980164{col 67}{space 3} 6.918291 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 2.107925{col 26}{space 2} .1484834{col 37}{space 1} 14.20{col 46}{space 3}0.000{col 54}{space 4} 1.814816{col 67}{space 3} 2.401034 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} 8.762725{col 26}{space 2} .1532533{col 37}{space 1} 57.18{col 46}{space 3}0.000{col 54}{space 4} 8.460201{col 67}{space 3} 9.06525 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 9.067454{col 26}{space 2} .1944522{col 37}{space 1} 46.63{col 46}{space 3}0.000{col 54}{space 4} 8.683602{col 67}{space 3} 9.451306 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-29.01926{col 26}{space 2} 1.719281{col 37}{space 1} -16.88{col 46}{space 3}0.000{col 54}{space 4}-32.41315{col 67}{space 3}-25.62537 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 755 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.761 {res}First component regression {txt}(sum of wgt is 1.6353e+02) Linear regression Number of obs = {res} 214 {txt}{help j_robustsingular:F(30, 182) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.7679 {txt}Root MSE = {res} .1138 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2}-.0151089{col 26}{space 2} .0183046{col 37}{space 1} -0.83{col 46}{space 3}0.410{col 54}{space 4}-.0512254{col 67}{space 3} .0210077 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0111894{col 26}{space 2} .0045345{col 37}{space 1} -2.47{col 46}{space 3}0.015{col 54}{space 4}-.0201363{col 67}{space 3}-.0022424 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0177534{col 26}{space 2} .0177037{col 37}{space 1} -1.00{col 46}{space 3}0.317{col 54}{space 4}-.0526842{col 67}{space 3} .0171775 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0059501{col 26}{space 2} .0044278{col 37}{space 1} 1.34{col 46}{space 3}0.181{col 54}{space 4}-.0027862{col 67}{space 3} .0146865 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0007057{col 26}{space 2} .0017372{col 37}{space 1} 0.41{col 46}{space 3}0.685{col 54}{space 4} -.002722{col 67}{space 3} .0041334 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0584262{col 26}{space 2} .0943615{col 37}{space 1} 0.62{col 46}{space 3}0.537{col 54}{space 4} -.127757{col 67}{space 3} .2446094 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0642124{col 26}{space 2} .0397005{col 37}{space 1} -1.62{col 46}{space 3}0.108{col 54}{space 4}-.1425448{col 67}{space 3} .0141199 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0902042{col 26}{space 2} .0415288{col 37}{space 1} -2.17{col 46}{space 3}0.031{col 54}{space 4} -.172144{col 67}{space 3}-.0082643 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} -.053067{col 26}{space 2} .0340141{col 37}{space 1} -1.56{col 46}{space 3}0.120{col 54}{space 4}-.1201797{col 67}{space 3} .0140457 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0273057{col 26}{space 2} .037954{col 37}{space 1} -0.72{col 46}{space 3}0.473{col 54}{space 4} -.102192{col 67}{space 3} .0475807 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0995719{col 26}{space 2} .039877{col 37}{space 1} -2.50{col 46}{space 3}0.013{col 54}{space 4}-.1782525{col 67}{space 3}-.0208912 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0556271{col 26}{space 2} .0456913{col 37}{space 1} 1.22{col 46}{space 3}0.225{col 54}{space 4}-.0345256{col 67}{space 3} .1457798 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .014949{col 26}{space 2} .0393753{col 37}{space 1} 0.38{col 46}{space 3}0.705{col 54}{space 4}-.0627418{col 67}{space 3} .0926398 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.0135734{col 26}{space 2} .0279438{col 37}{space 1} -0.49{col 46}{space 3}0.628{col 54}{space 4}-.0687088{col 67}{space 3} .041562 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0067797{col 26}{space 2} .0372674{col 37}{space 1} -0.18{col 46}{space 3}0.856{col 54}{space 4}-.0803114{col 67}{space 3} .0667519 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0449629{col 26}{space 2} .0310579{col 37}{space 1} -1.45{col 46}{space 3}0.149{col 54}{space 4}-.1062427{col 67}{space 3} .016317 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0118442{col 26}{space 2} .0332585{col 37}{space 1} -0.36{col 46}{space 3}0.722{col 54}{space 4}-.0774661{col 67}{space 3} .0537776 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .03008{col 26}{space 2} .0445825{col 37}{space 1} 0.67{col 46}{space 3}0.501{col 54}{space 4}-.0578851{col 67}{space 3} .118045 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0193341{col 26}{space 2} .0440052{col 37}{space 1} -0.44{col 46}{space 3}0.661{col 54}{space 4}-.1061601{col 67}{space 3} .0674919 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.0329871{col 26}{space 2} .0709792{col 37}{space 1} -0.46{col 46}{space 3}0.643{col 54}{space 4} -.173035{col 67}{space 3} .1070608 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .0545667{col 26}{space 2} .0644599{col 37}{space 1} 0.85{col 46}{space 3}0.398{col 54}{space 4}-.0726181{col 67}{space 3} .1817515 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .0468734{col 26}{space 2} .0595298{col 37}{space 1} 0.79{col 46}{space 3}0.432{col 54}{space 4}-.0705838{col 67}{space 3} .1643307 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.0711913{col 26}{space 2} .0685905{col 37}{space 1} -1.04{col 46}{space 3}0.301{col 54}{space 4}-.2065262{col 67}{space 3} .0641435 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .6644704{col 26}{space 2} .0898558{col 37}{space 1} 7.39{col 46}{space 3}0.000{col 54}{space 4} .4871774{col 67}{space 3} .8417634 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 1.786234{col 26}{space 2} .0675352{col 37}{space 1} 26.45{col 46}{space 3}0.000{col 54}{space 4} 1.652982{col 67}{space 3} 1.919487 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} -.040843{col 26}{space 2} .0553147{col 37}{space 1} -0.74{col 46}{space 3}0.461{col 54}{space 4}-.1499836{col 67}{space 3} .0682976 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1374354{col 26}{space 2} .0768293{col 37}{space 1} 1.79{col 46}{space 3}0.075{col 54}{space 4}-.0141552{col 67}{space 3} .289026 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0074925{col 26}{space 2} .0599444{col 37}{space 1} -0.12{col 46}{space 3}0.901{col 54}{space 4} -.125768{col 67}{space 3} .1107829 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .0949596{col 26}{space 2} .0637937{col 37}{space 1} 1.49{col 46}{space 3}0.138{col 54}{space 4}-.0309108{col 67}{space 3} .22083 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0441287{col 26}{space 2} .0509914{col 37}{space 1} -0.87{col 46}{space 3}0.388{col 54}{space 4}-.1447391{col 67}{space 3} .0564817 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3220609{col 26}{space 2} .1125651{col 37}{space 1} 2.86{col 46}{space 3}0.005{col 54}{space 4} .0999606{col 67}{space 3} .5441612 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0941642{col 26}{space 2} .0510115{col 37}{space 1} 1.85{col 46}{space 3}0.067{col 54}{space 4}-.0064857{col 67}{space 3} .1948141 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 5.1469e+01) Linear regression Number of obs = {res} 158 {txt}{help j_robustsingular:F(30, 126) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9891 {txt}Root MSE = {res} .18713 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} 1.015601{col 26}{space 2} .0309506{col 37}{space 1} 32.81{col 46}{space 3}0.000{col 54}{space 4} .9543507{col 67}{space 3} 1.076851 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0298498{col 26}{space 2} .0185544{col 37}{space 1} -1.61{col 46}{space 3}0.110{col 54}{space 4}-.0665685{col 67}{space 3} .0068688 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.1701217{col 26}{space 2} .0527711{col 37}{space 1} -3.22{col 46}{space 3}0.002{col 54}{space 4}-.2745541{col 67}{space 3}-.0656892 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0154179{col 26}{space 2} .0152017{col 37}{space 1} 1.01{col 46}{space 3}0.312{col 54}{space 4}-.0146658{col 67}{space 3} .0455017 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0347002{col 26}{space 2} .0038997{col 37}{space 1} 8.90{col 46}{space 3}0.000{col 54}{space 4} .0269828{col 67}{space 3} .0424176 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .3855768{col 26}{space 2} .101431{col 37}{space 1} 3.80{col 46}{space 3}0.000{col 54}{space 4} .1848479{col 67}{space 3} .5863057 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .4077103{col 26}{space 2} .3098194{col 37}{space 1} 1.32{col 46}{space 3}0.191{col 54}{space 4}-.2054131{col 67}{space 3} 1.020834 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .2070328{col 26}{space 2} .1362681{col 37}{space 1} 1.52{col 46}{space 3}0.131{col 54}{space 4}-.0626378{col 67}{space 3} .4767034 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .2319588{col 26}{space 2} .1381105{col 37}{space 1} 1.68{col 46}{space 3}0.096{col 54}{space 4}-.0413579{col 67}{space 3} .5052754 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .5819955{col 26}{space 2} .1389253{col 37}{space 1} 4.19{col 46}{space 3}0.000{col 54}{space 4} .3070665{col 67}{space 3} .8569246 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .4662638{col 26}{space 2} .1336687{col 37}{space 1} 3.49{col 46}{space 3}0.001{col 54}{space 4} .2017374{col 67}{space 3} .7307902 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-.2474445{col 26}{space 2} .1023519{col 37}{space 1} -2.42{col 46}{space 3}0.017{col 54}{space 4}-.4499958{col 67}{space 3}-.0448931 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} -.196703{col 26}{space 2} .0867458{col 37}{space 1} -2.27{col 46}{space 3}0.025{col 54}{space 4}-.3683703{col 67}{space 3}-.0250356 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .6350095{col 26}{space 2} .2779928{col 37}{space 1} 2.28{col 46}{space 3}0.024{col 54}{space 4} .0848699{col 67}{space 3} 1.185149 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0904657{col 26}{space 2} .1687221{col 37}{space 1} -0.54{col 46}{space 3}0.593{col 54}{space 4}-.4243618{col 67}{space 3} .2434303 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .3176251{col 26}{space 2} .0954912{col 37}{space 1} 3.33{col 46}{space 3}0.001{col 54}{space 4} .1286508{col 67}{space 3} .5065994 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .1449837{col 26}{space 2} .1208949{col 37}{space 1} 1.20{col 46}{space 3}0.233{col 54}{space 4}-.0942638{col 67}{space 3} .3842311 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .4147293{col 26}{space 2} .1052933{col 37}{space 1} 3.94{col 46}{space 3}0.000{col 54}{space 4} .2063569{col 67}{space 3} .6231016 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0500023{col 26}{space 2} .1065719{col 37}{space 1} -0.47{col 46}{space 3}0.640{col 54}{space 4} -.260905{col 67}{space 3} .1609003 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.2579981{col 26}{space 2} .1722854{col 37}{space 1} -1.50{col 46}{space 3}0.137{col 54}{space 4}-.5989458{col 67}{space 3} .0829496 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .1532581{col 26}{space 2} .2371659{col 37}{space 1} 0.65{col 46}{space 3}0.519{col 54}{space 4}-.3160863{col 67}{space 3} .6226025 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .0523668{col 26}{space 2} .1846376{col 37}{space 1} 0.28{col 46}{space 3}0.777{col 54}{space 4}-.3130255{col 67}{space 3} .4177591 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .6131279{col 26}{space 2} .2533646{col 37}{space 1} 2.42{col 46}{space 3}0.017{col 54}{space 4} .1117268{col 67}{space 3} 1.114529 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5538667{col 26}{space 2} .2024285{col 37}{space 1} 2.74{col 46}{space 3}0.007{col 54}{space 4} .1532667{col 67}{space 3} .9544668 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .1100454{col 26}{space 2} .1255059{col 37}{space 1} 0.88{col 46}{space 3}0.382{col 54}{space 4}-.1383272{col 67}{space 3} .3584179 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} .5346685{col 26}{space 2} .1873143{col 37}{space 1} 2.85{col 46}{space 3}0.005{col 54}{space 4} .163979{col 67}{space 3} .9053579 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 2.884161{col 26}{space 2} .2045039{col 37}{space 1} 14.10{col 46}{space 3}0.000{col 54}{space 4} 2.479454{col 67}{space 3} 3.288869 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 1.356068{col 26}{space 2} .1372051{col 37}{space 1} 9.88{col 46}{space 3}0.000{col 54}{space 4} 1.084543{col 67}{space 3} 1.627592 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .2355685{col 26}{space 2} .1176557{col 37}{space 1} 2.00{col 46}{space 3}0.047{col 54}{space 4} .0027314{col 67}{space 3} .4684056 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .0429017{col 26}{space 2} .0878967{col 37}{space 1} 0.49{col 46}{space 3}0.626{col 54}{space 4}-.1310432{col 67}{space 3} .2168466 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .8802499{col 26}{space 2} .4637508{col 37}{space 1} 1.90{col 46}{space 3}0.060{col 54}{space 4}-.0374994{col 67}{space 3} 1.797999 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3169673{col 26}{space 2} .1117112{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.5380404{col 67}{space 3}-.0958941 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(215 real changes made) {res}This Switching Regression took {com}32{res} seconds. {txt}(195 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 54}{space 4}-.4009293{col 67}{space 3} .3813382 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2167708{col 26}{space 2} .1976057{col 37}{space 1} 1.10{col 46}{space 3}0.274{col 54}{space 4}-.1730119{col 67}{space 3} .6065536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1537051{col 26}{space 2} .1971893{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.5426666{col 67}{space 3} .2352564 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0596839{col 26}{space 2} .1954351{col 37}{space 1} 0.31{col 46}{space 3}0.760{col 54}{space 4}-.3258174{col 67}{space 3} .4451852 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0358223{col 26}{space 2} .1980381{col 37}{space 1} 0.18{col 46}{space 3}0.857{col 54}{space 4}-.3548133{col 67}{space 3} .426458 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2612704{col 26}{space 2} .1923846{col 37}{space 1} 1.36{col 46}{space 3}0.176{col 54}{space 4}-.1182137{col 67}{space 3} .6407544 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .172893{col 26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00080 Average of the probability vector is: {txt}0.736 On iteration {com}50{txt} greatest diff is: {com}0.012222 on tangi in the{txt} second main {com}eqn {txt}Log-likelihood is : 134.19675 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00023 Average of the probability vector is: {txt}0.736 On iteration {com}100{txt} greatest diff is: {com}0.001287 on tangi in the{txt} second main {com}eqn {txt}Log-likelihood is : 137.13778 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00014 Average of the probability vector is: {txt}0.737 On iteration {com}150{txt} greatest diff is: {com}0.000729 on tangi in the{txt} second main {com}eqn {txt}Log-likelihood is : 138.38587 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.737 On iteration {com}200{txt} greatest diff is: {com}-0.002467 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 139.21385 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.737 On iteration {com}250{txt} greatest diff is: {com}-0.004244 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 139.74315 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.737 On iteration {com}300{txt} greatest diff is: {com}-0.002928 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.09744 {res}This iteration took {com}1{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.737 On iteration {com}350{txt} greatest diff is: {com}-0.002199 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.33732 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.737 On iteration {com}400{txt} greatest diff is: {com}-0.001653 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.52184 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.737 On iteration {com}450{txt} greatest diff is: {com}-0.001541 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.66223 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.737 On iteration {com}500{txt} greatest diff is: {com}-0.002198 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.7743 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.737 On iteration {com}550{txt} greatest diff is: {com}-0.001339 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.87145 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.737 On iteration {com}600{txt} greatest diff is: {com}-0.002686 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 140.94649 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.737 On iteration {com}650{txt} greatest diff is: {com}-0.001790 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.00856 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}700{txt} greatest diff is: {com}-0.001531 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.05613 {res}This iteration took {com}0{res} second. On iter 750 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}750{txt} greatest diff is: {com}-0.001321 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.0931 {res}This iteration took {com}0{res} second. On iter 800 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}800{txt} greatest diff is: {com}-0.001116 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.12306 {res}This iteration took {com}1{res} second. On iter 850 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}850{txt} greatest diff is: {com}-0.001036 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.1478 {res}This iteration took {com}0{res} second. On iter 900 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}900{txt} greatest diff is: {com}-0.000932 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.16799 {res}This iteration took {com}0{res} second. On iter 950 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}950{txt} greatest diff is: {com}-0.000778 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.18469 {res}This iteration took {com}0{res} second. On iter 1000 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.737 On iteration {com}1000{txt} greatest diff is: {com}-0.000762 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.19888 {res}This iteration took {com}0{res} second. On iter 1050 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1050{txt} greatest diff is: {com}-0.000847 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.21075 {res}This iteration took {com}0{res} second. On iter 1100 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1100{txt} greatest diff is: {com}-0.000651 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.22105 {res}This iteration took {com}0{res} second. On iter 1150 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1150{txt} greatest diff is: {com}-0.000577 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.22983 {res}This iteration took {com}0{res} second. On iter 1200 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1200{txt} greatest diff is: {com}-0.000528 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.23734 {res}This iteration took {com}0{res} second. On iter 1250 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1250{txt} greatest diff is: {com}-0.000486 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.24388 {res}This iteration took {com}0{res} second. On iter 1300 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1300{txt} greatest diff is: {com}-0.000425 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.24959 {res}This iteration took {com}0{res} second. On iter 1350 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1350{txt} greatest diff is: {com}-0.000402 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.25466 {res}This iteration took {com}0{res} second. On iter 1400 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1400{txt} greatest diff is: {com}-0.000354 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.25914 {res}This iteration took {com}0{res} second. On iter 1450 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1450{txt} greatest diff is: {com}-0.000346 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.26314 {res}This iteration took {com}0{res} second. On iter 1500 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1500{txt} greatest diff is: {com}-0.000306 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.26674 {res}This iteration took {com}0{res} second. On iter 1550 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1550{txt} greatest diff is: {com}-0.000282 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.26999 {res}This iteration took {com}0{res} second. On iter 1600 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1600{txt} greatest diff is: {com}-0.000244 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.27294 {res}This iteration took {com}0{res} second. On iter 1650 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1650{txt} greatest diff is: {com}-0.000248 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.27569 {res}This iteration took {com}0{res} second. On iter 1700 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1700{txt} greatest diff is: {com}-0.000305 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.27823 {res}This iteration took {com}0{res} second. On iter 1750 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1750{txt} greatest diff is: {com}-0.000250 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.2806 {res}This iteration took {com}0{res} second. On iter 1800 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1800{txt} greatest diff is: {com}-0.000238 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.28277 {res}This iteration took {com}0{res} second. On iter 1850 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1850{txt} greatest diff is: {com}-0.000224 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.28475 {res}This iteration took {com}0{res} second. On iter 1900 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1900{txt} greatest diff is: {com}-0.000212 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.28658 {res}This iteration took {com}0{res} second. On iter 1950 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}1950{txt} greatest diff is: {com}-0.000203 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.28824 {res}This iteration took {com}0{res} second. On iter 2000 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2000{txt} greatest diff is: {com}-0.000237 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.28982 {res}This iteration took {com}0{res} second. On iter 2050 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2050{txt} greatest diff is: {com}-0.000213 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29129 {res}This iteration took {com}0{res} second. On iter 2100 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2100{txt} greatest diff is: {com}-0.000200 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29263 {res}This iteration took {com}0{res} second. On iter 2150 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2150{txt} greatest diff is: {com}-0.000208 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29391 {res}This iteration took {com}0{res} second. On iter 2200 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2200{txt} greatest diff is: {com}-0.000174 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29511 {res}This iteration took {com}0{res} second. On iter 2250 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2250{txt} greatest diff is: {com}-0.000167 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29625 {res}This iteration took {com}0{res} second. On iter 2300 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2300{txt} greatest diff is: {com}-0.000160 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29732 {res}This iteration took {com}0{res} second. On iter 2350 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2350{txt} greatest diff is: {com}-0.000154 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29833 {res}This iteration took {com}0{res} second. On iter 2400 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2400{txt} greatest diff is: {com}-0.000148 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.29927 {res}This iteration took {com}0{res} second. On iter 2450 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2450{txt} greatest diff is: {com}-0.000143 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30016 {res}This iteration took {com}0{res} second. On iter 2500 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2500{txt} greatest diff is: {com}-0.000138 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.301 {res}This iteration took {com}0{res} second. On iter 2550 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2550{txt} greatest diff is: {com}-0.000134 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30179 {res}This iteration took {com}1{res} second. On iter 2600 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2600{txt} greatest diff is: {com}-0.000132 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30254 {res}This iteration took {com}0{res} second. On iter 2650 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2650{txt} greatest diff is: {com}-0.000132 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30321 {res}This iteration took {com}0{res} second. On iter 2700 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2700{txt} greatest diff is: {com}-0.000126 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30388 {res}This iteration took {com}0{res} second. On iter 2750 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2750{txt} greatest diff is: {com}-0.000123 on f3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30452 {res}This iteration took {com}1{res} second. On iter 2800 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2800{txt} greatest diff is: {com}-0.000114 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30512 {res}This iteration took {com}0{res} second. On iter 2850 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2850{txt} greatest diff is: {com}-0.000138 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30567 {res}This iteration took {com}0{res} second. On iter 2900 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2900{txt} greatest diff is: {com}-0.000128 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30622 {res}This iteration took {com}0{res} second. On iter 2950 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}2950{txt} greatest diff is: {com}-0.000195 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30674 {res}This iteration took {com}0{res} second. On iter 3000 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3000{txt} greatest diff is: {com}-0.000148 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30725 {res}This iteration took {com}0{res} second. On iter 3050 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3050{txt} greatest diff is: {com}-0.000140 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30773 {res}This iteration took {com}0{res} second. On iter 3100 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3100{txt} greatest diff is: {com}-0.000128 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.3082 {res}This iteration took {com}0{res} second. On iter 3150 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3150{txt} greatest diff is: {com}-0.000129 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30861 {res}This iteration took {com}0{res} second. On iter 3200 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3200{txt} greatest diff is: {com}-0.000116 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30904 {res}This iteration took {com}0{res} second. On iter 3250 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3250{txt} greatest diff is: {com}-0.000113 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30946 {res}This iteration took {com}0{res} second. On iter 3300 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3300{txt} greatest diff is: {com}-0.000109 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.30985 {res}This iteration took {com}0{res} second. On iter 3350 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3350{txt} greatest diff is: {com}-0.000106 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.31023 {res}This iteration took {com}0{res} second. On iter 3400 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 On iteration {com}3400{txt} greatest diff is: {com}-0.000102 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 141.31059 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 1.24e-53 1.85e-66 {txt} 5% {res} 1.56e-21 2.04e-59 {txt}10% {res} 1.15e-12 1.24e-53 {txt}Obs {res} 221 {txt}25% {res} .4664926 1.50e-52 {txt}Sum of Wgt. {res} 221 {txt}50% {res} .9999978 {txt}Mean {res} .7367361 {txt}Largest Std. Dev. {res} .3999936 {txt}75% {res} 1 1 {txt}90% {res} 1 1 {txt}Variance {res} .1599948 {txt}95% {res} 1 1 {txt}Skewness {res} -1.06121 {txt}99% {res} 1 1 {txt}Kurtosis {res} 2.340037 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 157 {txt}{hline 13}{c +}{hline 34} F(31, 125) = {res} 1169.42 {txt} Model {c |} {res} 5496.20409 31 177.296906 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 18.9514025 125 .15161122 {txt}R-squared ={res} 0.9966 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9957 {txt} Total {c |} {res} 5515.15549 156 35.3535608 {txt}Root MSE = {res} .38937 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 6}kz_cfs {c |}{col 14}{res}{space 2} 1.25459{col 26}{space 2} .0323939{col 37}{space 1} 38.73{col 46}{space 3}0.000{col 54}{space 4} 1.190479{col 67}{space 3} 1.318702 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.0466514{col 26}{space 2} .0040324{col 37}{space 1} -11.57{col 46}{space 3}0.000{col 54}{space 4}-.0546321{col 67}{space 3}-.0386708 {txt}{space 8}kz_d {c |}{col 14}{res}{space 2}-5.068392{col 26}{space 2} .2381597{col 37}{space 1} -21.28{col 46}{space 3}0.000{col 54}{space 4}-5.539739{col 67}{space 3}-4.597044 {txt}{space 6}kz_div {c |}{col 14}{res}{space 2} 4.706334{col 26}{space 2} .3670057{col 37}{space 1} 12.82{col 46}{space 3}0.000{col 54}{space 4} 3.979985{col 67}{space 3} 5.432684 {txt}{space 7}kz_cs {c |}{col 14}{res}{space 2} 2.524794{col 26}{space 2} .2308792{col 37}{space 1} 10.94{col 46}{space 3}0.000{col 54}{space 4} 2.067855{col 67}{space 3} 2.981732 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-3.663823{col 26}{space 2} .1615365{col 37}{space 1} -22.68{col 46}{space 3}0.000{col 54}{space 4}-3.983524{col 67}{space 3}-3.344122 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-3.467607{col 26}{space 2} .1610279{col 37}{space 1} -21.53{col 46}{space 3}0.000{col 54}{space 4}-3.786301{col 67}{space 3}-3.148913 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -8.07014{col 26}{space 2} .1472584{col 37}{space 1} -54.80{col 46}{space 3}0.000{col 54}{space 4}-8.361582{col 67}{space 3}-7.778697 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-4.028375{col 26}{space 2} .1621154{col 37}{space 1} -24.85{col 46}{space 3}0.000{col 54}{space 4}-4.349221{col 67}{space 3}-3.707528 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-6.791411{col 26}{space 2} .1432571{col 37}{space 1} -47.41{col 46}{space 3}0.000{col 54}{space 4}-7.074934{col 67}{space 3}-6.507887 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} 1.164656{col 26}{space 2} .2113174{col 37}{space 1} 5.51{col 46}{space 3}0.000{col 54}{space 4} .7464325{col 67}{space 3} 1.582879 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} 12.92831{col 26}{space 2} .4188406{col 37}{space 1} 30.87{col 46}{space 3}0.000{col 54}{space 4} 12.09937{col 67}{space 3} 13.75725 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .3101368{col 26}{space 2} .1764134{col 37}{space 1} 1.76{col 46}{space 3}0.081{col 54}{space 4}-.0390072{col 67}{space 3} .6592808 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} 1.086415{col 26}{space 2} .2113693{col 37}{space 1} 5.14{col 46}{space 3}0.000{col 54}{space 4} .6680892{col 67}{space 3} 1.504742 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-7.451256{col 26}{space 2} .1370132{col 37}{space 1} -54.38{col 46}{space 3}0.000{col 54}{space 4}-7.722422{col 67}{space 3} -7.18009 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-7.748026{col 26}{space 2} .1370041{col 37}{space 1} -56.55{col 46}{space 3}0.000{col 54}{space 4}-8.019174{col 67}{space 3}-7.476878 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-3.239723{col 26}{space 2} .1447219{col 37}{space 1} -22.39{col 46}{space 3}0.000{col 54}{space 4}-3.526146{col 67}{space 3} -2.9533 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-6.701281{col 26}{space 2} .1373437{col 37}{space 1} -48.79{col 46}{space 3}0.000{col 54}{space 4}-6.973102{col 67}{space 3}-6.429461 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} -6.10081{col 26}{space 2} .1401399{col 37}{space 1} -43.53{col 46}{space 3}0.000{col 54}{space 4}-6.378165{col 67}{space 3}-5.823456 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 10.35631{col 26}{space 2} .2271672{col 37}{space 1} 45.59{col 46}{space 3}0.000{col 54}{space 4} 9.906717{col 67}{space 3} 10.8059 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 6.868818{col 26}{space 2} .1863583{col 37}{space 1} 36.86{col 46}{space 3}0.000{col 54}{space 4} 6.499992{col 67}{space 3} 7.237644 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 4.652655{col 26}{space 2} .1740152{col 37}{space 1} 26.74{col 46}{space 3}0.000{col 54}{space 4} 4.308257{col 67}{space 3} 4.997052 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 10.47933{col 26}{space 2} .1990753{col 37}{space 1} 52.64{col 46}{space 3}0.000{col 54}{space 4} 10.08534{col 67}{space 3} 10.87333 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-1.562344{col 26}{space 2} .1411224{col 37}{space 1} -11.07{col 46}{space 3}0.000{col 54}{space 4}-1.841643{col 67}{space 3}-1.283045 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-9.403147{col 26}{space 2} .153665{col 37}{space 1} -61.19{col 46}{space 3}0.000{col 54}{space 4}-9.707269{col 67}{space 3}-9.099025 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 13.90375{col 26}{space 2} .1867527{col 37}{space 1} 74.45{col 46}{space 3}0.000{col 54}{space 4} 13.53414{col 67}{space 3} 14.27336 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 6.700693{col 26}{space 2} .1703165{col 37}{space 1} 39.34{col 46}{space 3}0.000{col 54}{space 4} 6.363616{col 67}{space 3} 7.037771 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 12.65133{col 26}{space 2} .2979216{col 37}{space 1} 42.47{col 46}{space 3}0.000{col 54}{space 4} 12.06171{col 67}{space 3} 13.24096 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 6.516488{col 26}{space 2} .1465625{col 37}{space 1} 44.46{col 46}{space 3}0.000{col 54}{space 4} 6.226423{col 67}{space 3} 6.806554 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} 8.980628{col 26}{space 2} .2015584{col 37}{space 1} 44.56{col 46}{space 3}0.000{col 54}{space 4} 8.581719{col 67}{space 3} 9.379538 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 6.45954{col 26}{space 2} .1566023{col 37}{space 1} 41.25{col 46}{space 3}0.000{col 54}{space 4} 6.149605{col 67}{space 3} 6.769476 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7714725{col 26}{space 2} .1436502{col 37}{space 1} 5.37{col 46}{space 3}0.000{col 54}{space 4} .4871709{col 67}{space 3} 1.055774 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 3429 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.737 {res}First component regression {txt}(sum of wgt is 1.6286e+02) Linear regression Number of obs = {res} 220 {txt}{help j_robustsingular:F(30, 188) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.7635 {txt}Root MSE = {res} .11145 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2}-.0164207{col 26}{space 2} .0168646{col 37}{space 1} -0.97{col 46}{space 3}0.331{col 54}{space 4}-.0496889{col 67}{space 3} .0168475 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0096849{col 26}{space 2} .0039637{col 37}{space 1} -2.44{col 46}{space 3}0.015{col 54}{space 4}-.0175039{col 67}{space 3}-.0018658 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0221939{col 26}{space 2} .0191104{col 37}{space 1} -1.16{col 46}{space 3}0.247{col 54}{space 4}-.0598922{col 67}{space 3} .0155044 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0057294{col 26}{space 2} .004426{col 37}{space 1} 1.29{col 46}{space 3}0.197{col 54}{space 4}-.0030016{col 67}{space 3} .0144603 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0009699{col 26}{space 2} .0019798{col 37}{space 1} 0.49{col 46}{space 3}0.625{col 54}{space 4}-.0029356{col 67}{space 3} .0048753 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0409166{col 26}{space 2} .0899403{col 37}{space 1} 0.45{col 46}{space 3}0.650{col 54}{space 4}-.1365054{col 67}{space 3} .2183386 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0662734{col 26}{space 2} .0377395{col 37}{space 1} -1.76{col 46}{space 3}0.081{col 54}{space 4}-.1407208{col 67}{space 3} .008174 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0467788{col 26}{space 2} .0322096{col 37}{space 1} -1.45{col 46}{space 3}0.148{col 54}{space 4}-.1103175{col 67}{space 3} .01676 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0443056{col 26}{space 2} .0345023{col 37}{space 1} -1.28{col 46}{space 3}0.201{col 54}{space 4} -.112367{col 67}{space 3} .0237558 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0572228{col 26}{space 2} .0246864{col 37}{space 1} -2.32{col 46}{space 3}0.022{col 54}{space 4}-.1059207{col 67}{space 3}-.0085248 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0826697{col 26}{space 2} .038953{col 37}{space 1} -2.12{col 46}{space 3}0.035{col 54}{space 4}-.1595109{col 67}{space 3}-.0058286 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0569103{col 26}{space 2} .0448951{col 37}{space 1} 1.27{col 46}{space 3}0.206{col 54}{space 4}-.0316526{col 67}{space 3} .1454731 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0214568{col 26}{space 2} .0388116{col 37}{space 1} 0.55{col 46}{space 3}0.581{col 54}{space 4}-.0551055{col 67}{space 3} .098019 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.0120431{col 26}{space 2} .0291947{col 37}{space 1} -0.41{col 46}{space 3}0.680{col 54}{space 4}-.0696343{col 67}{space 3} .0455481 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0047461{col 26}{space 2} .0386123{col 37}{space 1} -0.12{col 46}{space 3}0.902{col 54}{space 4}-.0809151{col 67}{space 3} .0714229 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0493454{col 26}{space 2} .0309538{col 37}{space 1} -1.59{col 46}{space 3}0.113{col 54}{space 4}-.1104067{col 67}{space 3} .0117159 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0078314{col 26}{space 2} .0339731{col 37}{space 1} -0.23{col 46}{space 3}0.818{col 54}{space 4} -.074849{col 67}{space 3} .0591861 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0342972{col 26}{space 2} .0466131{col 37}{space 1} 0.74{col 46}{space 3}0.463{col 54}{space 4}-.0576547{col 67}{space 3} .1262491 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0211462{col 26}{space 2} .044529{col 37}{space 1} -0.47{col 46}{space 3}0.635{col 54}{space 4}-.1089868{col 67}{space 3} .0666944 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.0058359{col 26}{space 2} .0694727{col 37}{space 1} -0.08{col 46}{space 3}0.933{col 54}{space 4}-.1428821{col 67}{space 3} .1312103 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .0782353{col 26}{space 2} .0627415{col 37}{space 1} 1.25{col 46}{space 3}0.214{col 54}{space 4}-.0455326{col 67}{space 3} .2020032 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .0686129{col 26}{space 2} .0570825{col 37}{space 1} 1.20{col 46}{space 3}0.231{col 54}{space 4}-.0439916{col 67}{space 3} .1812174 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.0342269{col 26}{space 2} .068165{col 37}{space 1} -0.50{col 46}{space 3}0.616{col 54}{space 4}-.1686934{col 67}{space 3} .1002396 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .6991517{col 26}{space 2} .1084643{col 37}{space 1} 6.45{col 46}{space 3}0.000{col 54}{space 4} .4851883{col 67}{space 3} .9131151 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 1.816865{col 26}{space 2} .0696543{col 37}{space 1} 26.08{col 46}{space 3}0.000{col 54}{space 4} 1.679461{col 67}{space 3} 1.95427 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0115773{col 26}{space 2} .0551959{col 37}{space 1} -0.21{col 46}{space 3}0.834{col 54}{space 4}-.1204602{col 67}{space 3} .0973056 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1695975{col 26}{space 2} .0759096{col 37}{space 1} 2.23{col 46}{space 3}0.027{col 54}{space 4} .0198536{col 67}{space 3} .3193415 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .0236479{col 26}{space 2} .0574018{col 37}{space 1} 0.41{col 46}{space 3}0.681{col 54}{space 4}-.0895864{col 67}{space 3} .1368822 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .0954847{col 26}{space 2} .0630444{col 37}{space 1} 1.51{col 46}{space 3}0.132{col 54}{space 4}-.0288806{col 67}{space 3} .21985 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0140763{col 26}{space 2} .0507652{col 37}{space 1} -0.28{col 46}{space 3}0.782{col 54}{space 4} -.114219{col 67}{space 3} .0860664 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3178962{col 26}{space 2} .1065805{col 37}{space 1} 2.98{col 46}{space 3}0.003{col 54}{space 4} .1076488{col 67}{space 3} .5281435 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0597293{col 26}{space 2} .0516632{col 37}{space 1} 1.16{col 46}{space 3}0.249{col 54}{space 4}-.0421848{col 67}{space 3} .1616434 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 5.8140e+01) Linear regression Number of obs = {res} 122 {txt}{help j_robustsingular:F(29, 90) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9885 {txt}Root MSE = {res} .19381 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} 1.020801{col 26}{space 2} .029369{col 37}{space 1} 34.76{col 46}{space 3}0.000{col 54}{space 4} .9624546{col 67}{space 3} 1.079148 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0172649{col 26}{space 2} .0169011{col 37}{space 1} -1.02{col 46}{space 3}0.310{col 54}{space 4}-.0508418{col 67}{space 3} .0163121 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.1476056{col 26}{space 2} .0575236{col 37}{space 1} -2.57{col 46}{space 3}0.012{col 54}{space 4}-.2618863{col 67}{space 3} -.033325 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .006125{col 26}{space 2} .0146988{col 37}{space 1} 0.42{col 46}{space 3}0.678{col 54}{space 4}-.0230768{col 67}{space 3} .0353268 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0335763{col 26}{space 2} .0035674{col 37}{space 1} 9.41{col 46}{space 3}0.000{col 54}{space 4} .026489{col 67}{space 3} .0406635 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .4509914{col 26}{space 2} .1074325{col 37}{space 1} 4.20{col 46}{space 3}0.000{col 54}{space 4} .237558{col 67}{space 3} .6644248 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .4018153{col 26}{space 2} .2359469{col 37}{space 1} 1.70{col 46}{space 3}0.092{col 54}{space 4}-.0669344{col 67}{space 3} .870565 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .2025153{col 26}{space 2} .120416{col 37}{space 1} 1.68{col 46}{space 3}0.096{col 54}{space 4}-.0367121{col 67}{space 3} .4417427 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .2075135{col 26}{space 2} .1295681{col 37}{space 1} 1.60{col 46}{space 3}0.113{col 54}{space 4}-.0498963{col 67}{space 3} .4649232 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .2045116{col 26}{space 2} .1262439{col 37}{space 1} 1.62{col 46}{space 3}0.109{col 54}{space 4}-.0462941{col 67}{space 3} .4553172 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .3733317{col 26}{space 2} .1596577{col 37}{space 1} 2.34{col 46}{space 3}0.022{col 54}{space 4} .0561439{col 67}{space 3} .6905196 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} -.304668{col 26}{space 2} .1033876{col 37}{space 1} -2.95{col 46}{space 3}0.004{col 54}{space 4}-.5100656{col 67}{space 3}-.0992705 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} -.229469{col 26}{space 2} .091689{col 37}{space 1} -2.50{col 46}{space 3}0.014{col 54}{space 4}-.4116253{col 67}{space 3}-.0473128 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .5632003{col 26}{space 2} .2710427{col 37}{space 1} 2.08{col 46}{space 3}0.041{col 54}{space 4} .0247267{col 67}{space 3} 1.101674 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.1441734{col 26}{space 2} .1719618{col 37}{space 1} -0.84{col 46}{space 3}0.404{col 54}{space 4}-.4858056{col 67}{space 3} .1974588 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2855393{col 26}{space 2} .0991206{col 37}{space 1} 2.88{col 46}{space 3}0.005{col 54}{space 4} .0886189{col 67}{space 3} .4824597 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0744727{col 26}{space 2} .1071487{col 37}{space 1} 0.70{col 46}{space 3}0.489{col 54}{space 4}-.1383969{col 67}{space 3} .2873423 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .3490249{col 26}{space 2} .1083168{col 37}{space 1} 3.22{col 46}{space 3}0.002{col 54}{space 4} .1338346{col 67}{space 3} .5642152 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.1092382{col 26}{space 2} .1068339{col 37}{space 1} -1.02{col 46}{space 3}0.309{col 54}{space 4}-.3214823{col 67}{space 3} .103006 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.3015755{col 26}{space 2} .1505761{col 37}{space 1} -2.00{col 46}{space 3}0.048{col 54}{space 4}-.6007212{col 67}{space 3}-.0024297 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .0255188{col 26}{space 2} .2307569{col 37}{space 1} 0.11{col 46}{space 3}0.912{col 54}{space 4}-.4329202{col 67}{space 3} .4839577 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.0739798{col 26}{space 2} .1661772{col 37}{space 1} -0.45{col 46}{space 3}0.657{col 54}{space 4}-.4041198{col 67}{space 3} .2561602 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .6398297{col 26}{space 2} .1954374{col 37}{space 1} 3.27{col 46}{space 3}0.002{col 54}{space 4} .2515592{col 67}{space 3} 1.0281 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .4197627{col 26}{space 2} .1556399{col 37}{space 1} 2.70{col 46}{space 3}0.008{col 54}{space 4} .1105568{col 67}{space 3} .7289686 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .0763043{col 26}{space 2} .1054012{col 37}{space 1} 0.72{col 46}{space 3}0.471{col 54}{space 4}-.1330936{col 67}{space 3} .2857022 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} .0700059{col 26}{space 2} .0983427{col 37}{space 1} 0.71{col 46}{space 3}0.478{col 54}{space 4}-.1253691{col 67}{space 3} .2653809 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 2.772246{col 26}{space 2} .1911738{col 37}{space 1} 14.50{col 46}{space 3}0.000{col 54}{space 4} 2.392446{col 67}{space 3} 3.152046 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 1.357311{col 26}{space 2} .1348597{col 37}{space 1} 10.06{col 46}{space 3}0.000{col 54}{space 4} 1.089388{col 67}{space 3} 1.625233 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .2839496{col 26}{space 2} .1134013{col 37}{space 1} 2.50{col 46}{space 3}0.014{col 54}{space 4} .0586581{col 67}{space 3} .5092412 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .0102673{col 26}{space 2} .0860771{col 37}{space 1} 0.12{col 46}{space 3}0.905{col 54}{space 4}-.1607398{col 67}{space 3} .1812743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1078362{col 26}{space 2} .252367{col 37}{space 1} -0.43{col 46}{space 3}0.670{col 54}{space 4}-.6092073{col 67}{space 3} .3935348 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2756412{col 26}{space 2} .1060999{col 37}{space 1} -2.60{col 46}{space 3}0.011{col 54}{space 4}-.4864272{col 67}{space 3}-.0648552 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(221 real changes made) {res}This Switching Regression took {com}2{res} minutes, {com}29{res} seconds. {txt}(162 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 54}{space 4}-.4009293{col 67}{space 3} .3813382 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2167708{col 26}{space 2} .1976057{col 37}{space 1} 1.10{col 46}{space 3}0.274{col 54}{space 4}-.1730119{col 67}{space 3} .6065536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1537051{col 26}{space 2} .1971893{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.5426666{col 67}{space 3} .2352564 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0596839{col 26}{space 2} .1954351{col 37}{space 1} 0.31{col 46}{space 3}0.760{col 54}{space 4}-.3258174{col 67}{space 3} .4451852 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0358223{col 26}{space 2} .1980381{col 37}{space 1} 0.18{col 46}{space 3}0.857{col 54}{space 4}-.3548133{col 67}{space 3} .426458 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2612704{col 26}{space 2} .1923846{col 37}{space 1} 1.36{col 46}{space 3}0.176{col 54}{space 4}-.1182137{col 67}{space 3} .6407544 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .172893{col 26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00063 Average of the probability vector is: {txt}0.750 On iteration {com}50{txt} greatest diff is: {com}0.080244 on yr4 in the{txt} second main {com}eqn {txt}Log-likelihood is : 92.321452 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00024 Average of the probability vector is: {txt}0.753 On iteration {com}100{txt} greatest diff is: {com}-0.007626 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 95.677882 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00053 Average of the probability vector is: {txt}0.745 On iteration {com}150{txt} greatest diff is: {com}0.028298 on yr3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 98.282402 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00024 Average of the probability vector is: {txt}0.751 On iteration {com}200{txt} greatest diff is: {com}0.002457 on yr6 in the{txt} first main {com}eqn {txt}Log-likelihood is : 101.81464 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00014 Average of the probability vector is: {txt}0.751 On iteration {com}250{txt} greatest diff is: {com}0.000630 on yr3 in the{txt} second main {com}eqn {txt}Log-likelihood is : 103.53553 {res}This iteration took {com}1{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.751 On iteration {com}300{txt} greatest diff is: {com}0.000507 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 104.80573 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.752 On iteration {com}350{txt} greatest diff is: {com}0.000546 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 105.91548 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.752 On iteration {com}400{txt} greatest diff is: {com}0.000298 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 106.82747 {res}This iteration took {com}1{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.753 On iteration {com}450{txt} greatest diff is: {com}0.000351 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 107.62018 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.753 On iteration {com}500{txt} greatest diff is: {com}0.000429 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 108.30106 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.753 On iteration {com}550{txt} greatest diff is: {com}0.000223 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 108.90075 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.753 On iteration {com}600{txt} greatest diff is: {com}0.000207 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 109.40694 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.753 On iteration {com}650{txt} greatest diff is: {com}0.000160 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 109.8474 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.753 On iteration {com}700{txt} greatest diff is: {com}0.000126 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 110.22672 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 5.33e-90 2.6e-191 {txt} 5% {res} 1.76e-33 5.0e-124 {txt}10% {res} 8.26e-15 5.33e-90 {txt}Obs {res} 206 {txt}25% {res} .6340201 3.43e-80 {txt}Sum of Wgt. {res} 206 {txt}50% {res} 1 {txt}Mean {res} .7530967 {txt}Largest Std. Dev. {res} .4131816 {txt}75% {res} 1 1 {txt}90% {res} 1 1 {txt}Variance {res} .1707191 {txt}95% {res} 1 1 {txt}Skewness {res}-1.179228 {txt}99% {res} 1 1 {txt}Kurtosis {res} 2.461073 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 117 {txt}{hline 13}{c +}{hline 34} F(32, 84) = {res} 6324.52 {txt} Model {c |} {res} 6525.54971 32 203.923429 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 2.70843698 84 .032243297 {txt}R-squared ={res} 0.9996 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9994 {txt} Total {c |} {res} 6528.25815 116 56.2780875 {txt}Root MSE = {res} .17956 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 8}ww_d {c |}{col 14}{res}{space 2}-23.72502{col 26}{space 2} .143624{col 37}{space 1} -165.19{col 46}{space 3}0.000{col 54}{space 4}-24.01063{col 67}{space 3} -23.4394 {txt}{space 6}ww_div {c |}{col 14}{res}{space 2}-4.650214{col 26}{space 2} .0999002{col 37}{space 1} -46.55{col 46}{space 3}0.000{col 54}{space 4}-4.848876{col 67}{space 3}-4.451551 {txt}{space 3}ww_gsales {c |}{col 14}{res}{space 2} 4.640295{col 26}{space 2} .0934818{col 37}{space 1} 49.64{col 46}{space 3}0.000{col 54}{space 4} 4.454396{col 67}{space 3} 4.826193 {txt}{space 5}ww_size {c |}{col 14}{res}{space 2} 6.860777{col 26}{space 2} .0551177{col 37}{space 1} 124.48{col 46}{space 3}0.000{col 54}{space 4} 6.751169{col 67}{space 3} 6.970384 {txt}{space 7}ww_cs {c |}{col 14}{res}{space 2} 16.14304{col 26}{space 2} .1871922{col 37}{space 1} 86.24{col 46}{space 3}0.000{col 54}{space 4} 15.77079{col 67}{space 3} 16.51529 {txt}{space 6}ww_cfs {c |}{col 14}{res}{space 2} -13.1045{col 26}{space 2} .1539883{col 37}{space 1} -85.10{col 46}{space 3}0.000{col 54}{space 4}-13.41072{col 67}{space 3}-12.79828 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 5.069828{col 26}{space 2} .1013043{col 37}{space 1} 50.05{col 46}{space 3}0.000{col 54}{space 4} 4.868373{col 67}{space 3} 5.271282 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} 7.901885{col 26}{space 2} .1371648{col 37}{space 1} 57.61{col 46}{space 3}0.000{col 54}{space 4} 7.629118{col 67}{space 3} 8.174652 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} 14.36329{col 26}{space 2} .1589103{col 37}{space 1} 90.39{col 46}{space 3}0.000{col 54}{space 4} 14.04728{col 67}{space 3} 14.6793 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} 6.654543{col 26}{space 2} .1011055{col 37}{space 1} 65.82{col 46}{space 3}0.000{col 54}{space 4} 6.453484{col 67}{space 3} 6.855603 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} 15.69851{col 26}{space 2} .1595437{col 37}{space 1} 98.40{col 46}{space 3}0.000{col 54}{space 4} 15.38124{col 67}{space 3} 16.01578 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} 5.492431{col 26}{space 2} .1168832{col 37}{space 1} 46.99{col 46}{space 3}0.000{col 54}{space 4} 5.259996{col 67}{space 3} 5.724866 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .3530272{col 26}{space 2} .0701134{col 37}{space 1} 5.04{col 46}{space 3}0.000{col 54}{space 4} .213599{col 67}{space 3} .4924555 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} 18.2415{col 26}{space 2} .1476946{col 37}{space 1} 123.51{col 46}{space 3}0.000{col 54}{space 4} 17.94779{col 67}{space 3} 18.53521 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} 19.0939{col 26}{space 2} .1443391{col 37}{space 1} 132.29{col 46}{space 3}0.000{col 54}{space 4} 18.80686{col 67}{space 3} 19.38093 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} 7.484267{col 26}{space 2} .1155309{col 37}{space 1} 64.78{col 46}{space 3}0.000{col 54}{space 4} 7.254521{col 67}{space 3} 7.714013 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-3.448385{col 26}{space 2} .0692649{col 37}{space 1} -49.79{col 46}{space 3}0.000{col 54}{space 4}-3.586126{col 67}{space 3}-3.310644 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .162438{col 26}{space 2} .0701469{col 37}{space 1} 2.32{col 46}{space 3}0.023{col 54}{space 4} .0229432{col 67}{space 3} .3019329 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-10.73855{col 26}{space 2} .063011{col 37}{space 1} -170.42{col 46}{space 3}0.000{col 54}{space 4}-10.86386{col 67}{space 3}-10.61325 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-2.474308{col 26}{space 2} .0628357{col 37}{space 1} -39.38{col 46}{space 3}0.000{col 54}{space 4}-2.599264{col 67}{space 3}-2.349352 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 22.99199{col 26}{space 2} .1421783{col 37}{space 1} 161.71{col 46}{space 3}0.000{col 54}{space 4} 22.70925{col 67}{space 3} 23.27473 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .6708057{col 26}{space 2} .1119235{col 37}{space 1} 5.99{col 46}{space 3}0.000{col 54}{space 4} .4482336{col 67}{space 3} .8933778 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 18.89357{col 26}{space 2} .1095071{col 37}{space 1} 172.53{col 46}{space 3}0.000{col 54}{space 4} 18.6758{col 67}{space 3} 19.11133 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 22.86653{col 26}{space 2} .1355804{col 37}{space 1} 168.66{col 46}{space 3}0.000{col 54}{space 4} 22.59691{col 67}{space 3} 23.13614 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} 15.28822{col 26}{space 2} .1140683{col 37}{space 1} 134.03{col 46}{space 3}0.000{col 54}{space 4} 15.06138{col 67}{space 3} 15.51506 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 5.933745{col 26}{space 2} .0963828{col 37}{space 1} 61.56{col 46}{space 3}0.000{col 54}{space 4} 5.742077{col 67}{space 3} 6.125413 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 19.96106{col 26}{space 2} .1052464{col 37}{space 1} 189.66{col 46}{space 3}0.000{col 54}{space 4} 19.75177{col 67}{space 3} 20.17035 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 13.05212{col 26}{space 2} .1175313{col 37}{space 1} 111.05{col 46}{space 3}0.000{col 54}{space 4} 12.8184{col 67}{space 3} 13.28585 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 18.46856{col 26}{space 2} .1910132{col 37}{space 1} 96.69{col 46}{space 3}0.000{col 54}{space 4} 18.08871{col 67}{space 3} 18.84841 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 11.87306{col 26}{space 2} .1045088{col 37}{space 1} 113.61{col 46}{space 3}0.000{col 54}{space 4} 11.66524{col 67}{space 3} 12.08089 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} 18.83136{col 26}{space 2} .1301011{col 37}{space 1} 144.74{col 46}{space 3}0.000{col 54}{space 4} 18.57264{col 67}{space 3} 19.09008 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 15.74872{col 26}{space 2} .1026962{col 37}{space 1} 153.35{col 46}{space 3}0.000{col 54}{space 4} 15.5445{col 67}{space 3} 15.95295 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-79.36974{col 26}{space 2} .6441392{col 37}{space 1} -123.22{col 46}{space 3}0.000{col 54}{space 4}-80.65068{col 67}{space 3} -78.0888 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 717 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.753 {res}First component regression {txt}(sum of wgt is 1.5495e+02) Linear regression Number of obs = {res} 206 {txt}{help j_robustsingular:F(29, 174) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.8976 {txt}Root MSE = {res} .16057 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .0217868{col 26}{space 2} .0169869{col 37}{space 1} 1.28{col 46}{space 3}0.201{col 54}{space 4}-.0117401{col 67}{space 3} .0553136 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0119272{col 26}{space 2} .0075006{col 37}{space 1} -1.59{col 46}{space 3}0.114{col 54}{space 4} -.026731{col 67}{space 3} .0028766 {txt}{space 4}profitab {c |}{col 14}{res}{space 2} -.007383{col 26}{space 2} .0157007{col 37}{space 1} -0.47{col 46}{space 3}0.639{col 54}{space 4}-.0383713{col 67}{space 3} .0236054 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0136197{col 26}{space 2} .0093763{col 37}{space 1} 1.45{col 46}{space 3}0.148{col 54}{space 4}-.0048862{col 67}{space 3} .0321255 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.0080219{col 26}{space 2} .0046741{col 37}{space 1} -1.72{col 46}{space 3}0.088{col 54}{space 4}-.0172472{col 67}{space 3} .0012034 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0914878{col 26}{space 2} .0600024{col 37}{space 1} -1.52{col 46}{space 3}0.129{col 54}{space 4} -.209914{col 67}{space 3} .0269385 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.1895425{col 26}{space 2} .0628444{col 37}{space 1} -3.02{col 46}{space 3}0.003{col 54}{space 4} -.313578{col 67}{space 3} -.065507 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0328347{col 26}{space 2} .0857324{col 37}{space 1} -0.38{col 46}{space 3}0.702{col 54}{space 4}-.2020441{col 67}{space 3} .1363747 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} -.151416{col 26}{space 2} .0607942{col 37}{space 1} -2.49{col 46}{space 3}0.014{col 54}{space 4} -.271405{col 67}{space 3}-.0314271 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .0049204{col 26}{space 2} .0535305{col 37}{space 1} 0.09{col 46}{space 3}0.927{col 54}{space 4}-.1007322{col 67}{space 3} .1105731 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0532163{col 26}{space 2} .0358103{col 37}{space 1} -1.49{col 46}{space 3}0.139{col 54}{space 4}-.1238949{col 67}{space 3} .0174622 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0685761{col 26}{space 2} .048568{col 37}{space 1} 1.41{col 46}{space 3}0.160{col 54}{space 4}-.0272821{col 67}{space 3} .1644343 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0088704{col 26}{space 2} .0569841{col 37}{space 1} -0.16{col 46}{space 3}0.876{col 54}{space 4}-.1213394{col 67}{space 3} .1035987 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0211568{col 26}{space 2} .0466678{col 37}{space 1} 0.45{col 46}{space 3}0.651{col 54}{space 4} -.070951{col 67}{space 3} .1132646 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .017318{col 26}{space 2} .0726565{col 37}{space 1} 0.24{col 46}{space 3}0.812{col 54}{space 4}-.1260836{col 67}{space 3} .1607195 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0405747{col 26}{space 2} .0357322{col 37}{space 1} -1.14{col 46}{space 3}0.258{col 54}{space 4}-.1110991{col 67}{space 3} .0299497 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0461293{col 26}{space 2} .036103{col 37}{space 1} -1.28{col 46}{space 3}0.203{col 54}{space 4}-.1173855{col 67}{space 3} .0251268 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} 4.179987{col 26}{space 2} .0422018{col 37}{space 1} 99.05{col 46}{space 3}0.000{col 54}{space 4} 4.096694{col 67}{space 3} 4.26328 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0721175{col 26}{space 2} .058469{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.0432822{col 67}{space 3} .1875173 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .0729851{col 26}{space 2} .0851333{col 37}{space 1} 0.86{col 46}{space 3}0.392{col 54}{space 4}-.0950418{col 67}{space 3} .2410119 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .2086865{col 26}{space 2} .1114495{col 37}{space 1} 1.87{col 46}{space 3}0.063{col 54}{space 4}-.0112805{col 67}{space 3} .4286535 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .2245978{col 26}{space 2} .1161503{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0046471{col 67}{space 3} .4538426 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .0502887{col 26}{space 2} .0887457{col 37}{space 1} 0.57{col 46}{space 3}0.572{col 54}{space 4} -.124868{col 67}{space 3} .2254454 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .3059099{col 26}{space 2} .1077083{col 37}{space 1} 2.84{col 46}{space 3}0.005{col 54}{space 4} .0933269{col 67}{space 3} .5184929 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 3.192051{col 26}{space 2} .1019422{col 37}{space 1} 31.31{col 46}{space 3}0.000{col 54}{space 4} 2.990849{col 67}{space 3} 3.393254 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0185855{col 26}{space 2} .0712749{col 37}{space 1} -0.26{col 46}{space 3}0.795{col 54}{space 4}-.1592601{col 67}{space 3} .1220891 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .488562{col 26}{space 2} .2063227{col 37}{space 1} 2.37{col 46}{space 3}0.019{col 54}{space 4} .0813446{col 67}{space 3} .8957795 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0382003{col 26}{space 2} .0734009{col 37}{space 1} -0.52{col 46}{space 3}0.603{col 54}{space 4}-.1830711{col 67}{space 3} .1066705 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .11213{col 26}{space 2} .0753622{col 37}{space 1} 1.49{col 46}{space 3}0.139{col 54}{space 4}-.0366117{col 67}{space 3} .2608717 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0243116{col 26}{space 2} .0682068{col 37}{space 1} -0.36{col 46}{space 3}0.722{col 54}{space 4}-.1589308{col 67}{space 3} .1103076 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .2628012{col 26}{space 2} .163771{col 37}{space 1} 1.60{col 46}{space 3}0.110{col 54}{space 4}-.0604322{col 67}{space 3} .5860346 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0806634{col 26}{space 2} .073121{col 37}{space 1} 1.10{col 46}{space 3}0.271{col 54}{space 4}-.0636548{col 67}{space 3} .2249816 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 5.1048e+01) Linear regression Number of obs = {res} 84 {txt}{help j_robustsingular:F(24, 52) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9946 {txt}Root MSE = {res} .14528 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .957533{col 26}{space 2} .022627{col 37}{space 1} 42.32{col 46}{space 3}0.000{col 54}{space 4} .9121286{col 67}{space 3} 1.002937 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0326758{col 26}{space 2} .0147152{col 37}{space 1} -2.22{col 46}{space 3}0.031{col 54}{space 4}-.0622041{col 67}{space 3}-.0031475 {txt}{space 4}profitab {c |}{col 14}{res}{space 2} -.293669{col 26}{space 2} .0413832{col 37}{space 1} -7.10{col 46}{space 3}0.000{col 54}{space 4}-.3767104{col 67}{space 3}-.2106275 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0763702{col 26}{space 2} .0080639{col 37}{space 1} 9.47{col 46}{space 3}0.000{col 54}{space 4} .0601887{col 67}{space 3} .0925517 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0135315{col 26}{space 2} .0046387{col 37}{space 1} 2.92{col 46}{space 3}0.005{col 54}{space 4} .0042233{col 67}{space 3} .0228398 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .4805672{col 26}{space 2} .1045645{col 37}{space 1} 4.60{col 46}{space 3}0.000{col 54}{space 4} .2707431{col 67}{space 3} .6903913 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0698724{col 26}{space 2} .0995089{col 37}{space 1} 0.70{col 46}{space 3}0.486{col 54}{space 4}-.1298069{col 67}{space 3} .2695517 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0766726{col 26}{space 2} .0923926{col 37}{space 1} 0.83{col 46}{space 3}0.410{col 54}{space 4}-.1087267{col 67}{space 3} .262072 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .3614469{col 26}{space 2} .1093378{col 37}{space 1} 3.31{col 46}{space 3}0.002{col 54}{space 4} .1420447{col 67}{space 3} .5808492 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .8231159{col 26}{space 2} .1242383{col 37}{space 1} 6.63{col 46}{space 3}0.000{col 54}{space 4} .5738134{col 67}{space 3} 1.072418 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .571099{col 26}{space 2} .1250238{col 37}{space 1} 4.57{col 46}{space 3}0.000{col 54}{space 4} .3202203{col 67}{space 3} .8219777 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2998587{col 26}{space 2} .1132846{col 37}{space 1} 2.65{col 46}{space 3}0.011{col 54}{space 4} .0725365{col 67}{space 3} .5271809 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .1802306{col 26}{space 2} .0848775{col 37}{space 1} 2.12{col 46}{space 3}0.038{col 54}{space 4} .0099115{col 67}{space 3} .3505496 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .3787916{col 26}{space 2} .1099628{col 37}{space 1} 3.44{col 46}{space 3}0.001{col 54}{space 4} .1581352{col 67}{space 3} .599448 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .2700184{col 26}{space 2} .1564341{col 37}{space 1} 1.73{col 46}{space 3}0.090{col 54}{space 4}-.0438897{col 67}{space 3} .5839264 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .3133005{col 26}{space 2} .0850272{col 37}{space 1} 3.68{col 46}{space 3}0.001{col 54}{space 4} .1426809{col 67}{space 3} .4839201 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .1706186{col 26}{space 2} .1171114{col 37}{space 1} 1.46{col 46}{space 3}0.151{col 54}{space 4}-.0643826{col 67}{space 3} .4056199 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .2938788{col 26}{space 2} .1706796{col 37}{space 1} 1.72{col 46}{space 3}0.091{col 54}{space 4}-.0486149{col 67}{space 3} .6363725 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0439395{col 26}{space 2} .1090217{col 37}{space 1} -0.40{col 46}{space 3}0.689{col 54}{space 4}-.2627075{col 67}{space 3} .1748285 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .9703627{col 26}{space 2} .1788055{col 37}{space 1} 5.43{col 46}{space 3}0.000{col 54}{space 4} .6115632{col 67}{space 3} 1.329162 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .8961797{col 26}{space 2} .2275177{col 37}{space 1} 3.94{col 46}{space 3}0.000{col 54}{space 4} .439632{col 67}{space 3} 1.352727 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .8262374{col 26}{space 2} .2081786{col 37}{space 1} 3.97{col 46}{space 3}0.000{col 54}{space 4} .4084965{col 67}{space 3} 1.243978 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 1.739063{col 26}{space 2} .2168964{col 37}{space 1} 8.02{col 46}{space 3}0.000{col 54}{space 4} 1.303829{col 67}{space 3} 2.174298 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .6157856{col 26}{space 2} .1864783{col 37}{space 1} 3.30{col 46}{space 3}0.002{col 54}{space 4} .2415895{col 67}{space 3} .9899817 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .0560086{col 26}{space 2} .1319164{col 37}{space 1} 0.42{col 46}{space 3}0.673{col 54}{space 4} -.208701{col 67}{space 3} .3207181 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.4005722{col 26}{space 2} .1525046{col 37}{space 1} -2.63{col 46}{space 3}0.011{col 54}{space 4} -.706595{col 67}{space 3}-.0945494 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .3037627{col 26}{space 2} .1183685{col 37}{space 1} 2.57{col 46}{space 3}0.013{col 54}{space 4} .0662388{col 67}{space 3} .5412865 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .7418018{col 26}{space 2} .2221108{col 37}{space 1} 3.34{col 46}{space 3}0.002{col 54}{space 4} .2961038{col 67}{space 3} 1.1875 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.3964429{col 26}{space 2} .1509656{col 37}{space 1} -2.63{col 46}{space 3}0.011{col 54}{space 4}-.6993775{col 67}{space 3}-.0935082 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .2213263{col 26}{space 2} .1487334{col 37}{space 1} 1.49{col 46}{space 3}0.143{col 54}{space 4} -.077129{col 67}{space 3} .5197817 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.8366561{col 26}{space 2} .3568817{col 37}{space 1} -2.34{col 46}{space 3}0.023{col 54}{space 4}-1.552792{col 67}{space 3}-.1205205 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4341774{col 26}{space 2} .1035682{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.6420023{col 67}{space 3}-.2263526 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(206 real changes made) {res}This Switching Regression took {com}32{res} seconds. {txt}(163 real changes made) {com}. local regime hpd kzd wwd {txt} {com}. foreach l of local regime {c -(} {txt} 2{com}. switchr poh `l' {txt} 3{com}. gen byte poh`l'=fc>0.5 //storing classification results in binary {txt} 4{com}. replace fc=diba //reseting the initial values {txt} 5{com}. {c )-} {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 54}{space 4}-.4009293{col 67}{space 3} .3813382 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2167708{col 26}{space 2} .1976057{col 37}{space 1} 1.10{col 46}{space 3}0.274{col 54}{space 4}-.1730119{col 67}{space 3} .6065536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1537051{col 26}{space 2} .1971893{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.5426666{col 67}{space 3} .2352564 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0596839{col 26}{space 2} .1954351{col 37}{space 1} 0.31{col 46}{space 3}0.760{col 54}{space 4}-.3258174{col 67}{space 3} .4451852 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0358223{col 26}{space 2} .1980381{col 37}{space 1} 0.18{col 46}{space 3}0.857{col 54}{space 4}-.3548133{col 67}{space 3} .426458 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2612704{col 26}{space 2} .1923846{col 37}{space 1} 1.36{col 46}{space 3}0.176{col 54}{space 4}-.1182137{col 67}{space 3} .6407544 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .172893{col 26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00027 Average of the probability vector is: {txt}0.268 On iteration {com}50{txt} greatest diff is: {com}0.019499 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 123.88893 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.267 On iteration {com}100{txt} greatest diff is: {com}-0.002169 on tangi in the{txt} first main {com}eqn {txt}Log-likelihood is : 120.92476 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.267 On iteration {com}150{txt} greatest diff is: {com}0.000577 on f12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 119.89081 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.267 On iteration {com}200{txt} greatest diff is: {com}0.000324 on f12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 119.37289 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}250{txt} greatest diff is: {com}-0.000285 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 119.04018 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}300{txt} greatest diff is: {com}-0.000227 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 118.79619 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}350{txt} greatest diff is: {com}-0.000175 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 118.60527 {res}This iteration took {com}1{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}400{txt} greatest diff is: {com}-0.000133 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 118.45043 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}450{txt} greatest diff is: {com}0.000113 on f12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 118.32188 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 On iteration {com}500{txt} greatest diff is: {com}0.000104 on f12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 118.21323 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 3.96e-10 6.86e-11 {txt} 5% {res} 2.72e-06 3.56e-10 {txt}10% {res} .0000163 3.96e-10 {txt}Obs {res} 201 {txt}25% {res} .0005708 9.01e-09 {txt}Sum of Wgt. {res} 201 {txt}50% {res} .125257 {txt}Mean {res} .2671052 {txt}Largest Std. Dev. {res} .33943 {txt}75% {res} .4426452 .9999688 {txt}90% {res} .9434137 .9999721 {txt}Variance {res} .1152127 {txt}95% {res} .9965055 .9999954 {txt}Skewness {res} 1.155651 {txt}99% {res} .9999721 .9999979 {txt}Kurtosis {res} 2.88978 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 201 {txt}{hline 13}{c +}{hline 34} F(29, 171) = {res} 134.17 {txt} Model {c |} {res} 975.121203 29 33.6248691 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 42.8555174 171 .250617061 {txt}R-squared ={res} 0.9579 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9508 {txt} Total {c |} {res} 1017.97672 200 5.0898836 {txt}Root MSE = {res} .50062 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 9}age {c |}{col 14}{res}{space 2} .5232265{col 26}{space 2} .1436043{col 37}{space 1} 3.64{col 46}{space 3}0.000{col 54}{space 4} .2397612{col 67}{space 3} .8066918 {txt}{space 8}size {c |}{col 14}{res}{space 2} 2.384073{col 26}{space 2} .3497979{col 37}{space 1} 6.82{col 46}{space 3}0.000{col 54}{space 4} 1.693595{col 67}{space 3} 3.074551 {txt}{space 7}size2 {c |}{col 14}{res}{space 2}-.0487538{col 26}{space 2} .0133007{col 37}{space 1} -3.67{col 46}{space 3}0.000{col 54}{space 4}-.0750085{col 67}{space 3}-.0224992 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-1.063563{col 26}{space 2} .1919722{col 37}{space 1} -5.54{col 46}{space 3}0.000{col 54}{space 4}-1.442503{col 67}{space 3}-.6846222 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.9603712{col 26}{space 2} .1890927{col 37}{space 1} -5.08{col 46}{space 3}0.000{col 54}{space 4}-1.333628{col 67}{space 3}-.5871147 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .1931868{col 26}{space 2} .1800887{col 37}{space 1} 1.07{col 46}{space 3}0.285{col 54}{space 4}-.1622964{col 67}{space 3} .5486701 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.1973034{col 26}{space 2} .1871061{col 37}{space 1} -1.05{col 46}{space 3}0.293{col 54}{space 4}-.5666384{col 67}{space 3} .1720317 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0763253{col 26}{space 2} .1738871{col 37}{space 1} -0.44{col 46}{space 3}0.661{col 54}{space 4} -.419567{col 67}{space 3} .2669163 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-1.976645{col 26}{space 2} .1714675{col 37}{space 1} -11.53{col 46}{space 3}0.000{col 54}{space 4} -2.31511{col 67}{space 3}-1.638179 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .5390397{col 26}{space 2} .1826878{col 37}{space 1} 2.95{col 46}{space 3}0.004{col 54}{space 4} .1784261{col 67}{space 3} .8996533 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0155285{col 26}{space 2} .1649492{col 37}{space 1} -0.09{col 46}{space 3}0.925{col 54}{space 4}-.3411273{col 67}{space 3} .3100702 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-2.426457{col 26}{space 2} .1637127{col 37}{space 1} -14.82{col 46}{space 3}0.000{col 54}{space 4}-2.749615{col 67}{space 3}-2.103299 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .4892857{col 26}{space 2} .1628676{col 37}{space 1} 3.00{col 46}{space 3}0.003{col 54}{space 4} .1677957{col 67}{space 3} .8107756 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .4518442{col 26}{space 2} .1677891{col 37}{space 1} 2.69{col 46}{space 3}0.008{col 54}{space 4} .1206396{col 67}{space 3} .7830488 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.7491809{col 26}{space 2} .1635038{col 37}{space 1} -4.58{col 46}{space 3}0.000{col 54}{space 4}-1.071927{col 67}{space 3}-.4264352 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .1623694{col 26}{space 2} .1686881{col 37}{space 1} 0.96{col 46}{space 3}0.337{col 54}{space 4}-.1706098{col 67}{space 3} .4953487 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .652669{col 26}{space 2} .1682776{col 37}{space 1} 3.88{col 46}{space 3}0.000{col 54}{space 4} .3205002{col 67}{space 3} .9848378 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} -5.48282{col 26}{space 2} .299036{col 37}{space 1} -18.33{col 46}{space 3}0.000{col 54}{space 4}-6.073097{col 67}{space 3}-4.892543 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-6.618392{col 26}{space 2} .2945932{col 37}{space 1} -22.47{col 46}{space 3}0.000{col 54}{space 4}-7.199899{col 67}{space 3}-6.036884 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-5.056585{col 26}{space 2} .2771958{col 37}{space 1} -18.24{col 46}{space 3}0.000{col 54}{space 4}-5.603752{col 67}{space 3}-4.509419 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} -4.95439{col 26}{space 2} .3066708{col 37}{space 1} -16.16{col 46}{space 3}0.000{col 54}{space 4}-5.559738{col 67}{space 3}-4.349043 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} -.097961{col 26}{space 2} .2282566{col 37}{space 1} -0.43{col 46}{space 3}0.668{col 54}{space 4}-.5485244{col 67}{space 3} .3526023 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.2693637{col 26}{space 2} .2922353{col 37}{space 1} -0.92{col 46}{space 3}0.358{col 54}{space 4}-.8462169{col 67}{space 3} .3074895 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-5.300273{col 26}{space 2} .2903019{col 37}{space 1} -18.26{col 46}{space 3}0.000{col 54}{space 4} -5.87331{col 67}{space 3}-4.727236 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-6.528784{col 26}{space 2} .3008547{col 37}{space 1} -21.70{col 46}{space 3}0.000{col 54}{space 4}-7.122651{col 67}{space 3}-5.934916 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.8073832{col 26}{space 2} .3225975{col 37}{space 1} -2.50{col 46}{space 3}0.013{col 54}{space 4}-1.444169{col 67}{space 3}-.1705971 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} -4.03698{col 26}{space 2} .2305329{col 37}{space 1} -17.51{col 46}{space 3}0.000{col 54}{space 4}-4.492037{col 67}{space 3}-3.581923 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-5.520185{col 26}{space 2} .199072{col 37}{space 1} -27.73{col 46}{space 3}0.000{col 54}{space 4} -5.91314{col 67}{space 3} -5.12723 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-3.208428{col 26}{space 2} .2749848{col 37}{space 1} -11.67{col 46}{space 3}0.000{col 54}{space 4} -3.75123{col 67}{space 3}-2.665626 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} -19.2029{col 26}{space 2} 2.366206{col 37}{space 1} -8.12{col 46}{space 3}0.000{col 54}{space 4}-23.87364{col 67}{space 3}-14.53217 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 524 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.267 {res}First component regression {txt}(sum of wgt is 5.3868e+01) Linear regression Number of obs = {res} 201 {txt}{help j_robustsingular:F(29, 169) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9839 {txt}Root MSE = {res} .20737 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .9640587{col 26}{space 2} .0433196{col 37}{space 1} 22.25{col 46}{space 3}0.000{col 54}{space 4} .8785416{col 67}{space 3} 1.049576 {txt}{space 7}sales {c |}{col 14}{res}{space 2} .0302252{col 26}{space 2} .016737{col 37}{space 1} 1.81{col 46}{space 3}0.073{col 54}{space 4}-.0028153{col 67}{space 3} .0632657 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0620256{col 26}{space 2} .0793785{col 37}{space 1} -0.78{col 46}{space 3}0.436{col 54}{space 4}-.2187267{col 67}{space 3} .0946755 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0010928{col 26}{space 2} .0237985{col 37}{space 1} 0.05{col 46}{space 3}0.963{col 54}{space 4}-.0458878{col 67}{space 3} .0480735 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0207264{col 26}{space 2} .0057305{col 37}{space 1} 3.62{col 46}{space 3}0.000{col 54}{space 4} .0094138{col 67}{space 3} .0320389 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 2.003634{col 26}{space 2} .2795323{col 37}{space 1} 7.17{col 46}{space 3}0.000{col 54}{space 4} 1.451809{col 67}{space 3} 2.555459 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .8423171{col 26}{space 2} .1575597{col 37}{space 1} 5.35{col 46}{space 3}0.000{col 54}{space 4} .5312783{col 67}{space 3} 1.153356 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0442273{col 26}{space 2} .1061336{col 37}{space 1} -0.42{col 46}{space 3}0.677{col 54}{space 4}-.2537457{col 67}{space 3} .1652912 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0579069{col 26}{space 2} .0867929{col 37}{space 1} -0.67{col 46}{space 3}0.506{col 54}{space 4}-.2292447{col 67}{space 3} .1134309 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.1094011{col 26}{space 2} .1116466{col 37}{space 1} -0.98{col 46}{space 3}0.329{col 54}{space 4}-.3298027{col 67}{space 3} .1110006 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .307502{col 26}{space 2} .0962848{col 37}{space 1} 3.19{col 46}{space 3}0.002{col 54}{space 4} .1174261{col 67}{space 3} .497578 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .1898121{col 26}{space 2} .2416283{col 37}{space 1} 0.79{col 46}{space 3}0.433{col 54}{space 4}-.2871864{col 67}{space 3} .6668106 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1370249{col 26}{space 2} .1397233{col 37}{space 1} -0.98{col 46}{space 3}0.328{col 54}{space 4}-.4128527{col 67}{space 3} .1388029 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .7952936{col 26}{space 2} .1414728{col 37}{space 1} 5.62{col 46}{space 3}0.000{col 54}{space 4} .5160121{col 67}{space 3} 1.074575 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.2638765{col 26}{space 2} .1757934{col 37}{space 1} -1.50{col 46}{space 3}0.135{col 54}{space 4}-.6109104{col 67}{space 3} .0831573 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .1472477{col 26}{space 2} .0804487{col 37}{space 1} 1.83{col 46}{space 3}0.069{col 54}{space 4}-.0115661{col 67}{space 3} .3060614 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.1243172{col 26}{space 2} .1205915{col 37}{space 1} -1.03{col 46}{space 3}0.304{col 54}{space 4}-.3623769{col 67}{space 3} .1137426 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .1626887{col 26}{space 2} .1011131{col 37}{space 1} 1.61{col 46}{space 3}0.109{col 54}{space 4}-.0369186{col 67}{space 3} .362296 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.1532857{col 26}{space 2} .1265111{col 37}{space 1} -1.21{col 46}{space 3}0.227{col 54}{space 4}-.4030314{col 67}{space 3} .0964599 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.6680045{col 26}{space 2} .2079583{col 37}{space 1} -3.21{col 46}{space 3}0.002{col 54}{space 4}-1.078535{col 67}{space 3}-.2574739 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.4361903{col 26}{space 2} .2712416{col 37}{space 1} -1.61{col 46}{space 3}0.110{col 54}{space 4}-.9716484{col 67}{space 3} .0992679 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.7970125{col 26}{space 2} .2494759{col 37}{space 1} -3.19{col 46}{space 3}0.002{col 54}{space 4}-1.289503{col 67}{space 3}-.3045221 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .6293571{col 26}{space 2} .2811702{col 37}{space 1} 2.24{col 46}{space 3}0.027{col 54}{space 4} .0742988{col 67}{space 3} 1.184415 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1485577{col 26}{space 2} .1758914{col 37}{space 1} -0.84{col 46}{space 3}0.400{col 54}{space 4}-.4957849{col 67}{space 3} .1986695 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} -.216873{col 26}{space 2} .1539474{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.5207806{col 67}{space 3} .0870346 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0521501{col 26}{space 2} .1013787{col 37}{space 1} -0.51{col 46}{space 3}0.608{col 54}{space 4}-.2522818{col 67}{space 3} .1479816 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 2.215938{col 26}{space 2} .2892495{col 37}{space 1} 7.66{col 46}{space 3}0.000{col 54}{space 4} 1.644931{col 67}{space 3} 2.786946 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 1.255915{col 26}{space 2} .1479532{col 37}{space 1} 8.49{col 46}{space 3}0.000{col 54}{space 4} .9638407{col 67}{space 3} 1.54799 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .2398003{col 26}{space 2} .149062{col 37}{space 1} 1.61{col 46}{space 3}0.110{col 54}{space 4}-.0544631{col 67}{space 3} .5340638 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .0434317{col 26}{space 2} .0957075{col 37}{space 1} 0.45{col 46}{space 3}0.651{col 54}{space 4}-.1455045{col 67}{space 3} .2323679 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.3244993{col 26}{space 2} .2365546{col 37}{space 1} -1.37{col 46}{space 3}0.172{col 54}{space 4}-.7914819{col 67}{space 3} .1424833 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1169842{col 26}{space 2} .0904617{col 37}{space 1} -1.29{col 46}{space 3}0.198{col 54}{space 4}-.2955646{col 67}{space 3} .0615963 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 1.4713e+02) Linear regression Number of obs = {res} 174 {txt}F(31, 142) = {res} 52.75 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.8089 {txt}Root MSE = {res} .06115 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .0068496{col 26}{space 2} .0062009{col 37}{space 1} 1.10{col 46}{space 3}0.271{col 54}{space 4}-.0054083{col 67}{space 3} .0191076 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0045852{col 26}{space 2} .0029605{col 37}{space 1} -1.55{col 46}{space 3}0.124{col 54}{space 4}-.0104376{col 67}{space 3} .0012671 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0064924{col 26}{space 2} .0071951{col 37}{space 1} -0.90{col 46}{space 3}0.368{col 54}{space 4}-.0207158{col 67}{space 3} .007731 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0002432{col 26}{space 2} .0016089{col 37}{space 1} 0.15{col 46}{space 3}0.880{col 54}{space 4}-.0029374{col 67}{space 3} .0034238 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.0008768{col 26}{space 2} .0007074{col 37}{space 1} -1.24{col 46}{space 3}0.217{col 54}{space 4}-.0022753{col 67}{space 3} .0005217 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0466626{col 26}{space 2} .0263262{col 37}{space 1} -1.77{col 46}{space 3}0.078{col 54}{space 4}-.0987044{col 67}{space 3} .0053793 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0415048{col 26}{space 2} .0235666{col 37}{space 1} -1.76{col 46}{space 3}0.080{col 54}{space 4}-.0880914{col 67}{space 3} .0050818 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.056343{col 26}{space 2} .0201361{col 37}{space 1} -2.80{col 46}{space 3}0.006{col 54}{space 4}-.0961484{col 67}{space 3}-.0165377 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0535657{col 26}{space 2} .0210246{col 37}{space 1} -2.55{col 46}{space 3}0.012{col 54}{space 4}-.0951274{col 67}{space 3} -.012004 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0438921{col 26}{space 2} .016926{col 37}{space 1} -2.59{col 46}{space 3}0.011{col 54}{space 4}-.0773515{col 67}{space 3}-.0104326 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0510789{col 26}{space 2} .0248367{col 37}{space 1} -2.06{col 46}{space 3}0.042{col 54}{space 4}-.1001764{col 67}{space 3}-.0019813 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-.0248324{col 26}{space 2} .016751{col 37}{space 1} -1.48{col 46}{space 3}0.140{col 54}{space 4} -.057946{col 67}{space 3} .0082812 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0076561{col 26}{space 2} .0188403{col 37}{space 1} -0.41{col 46}{space 3}0.685{col 54}{space 4}-.0448998{col 67}{space 3} .0295876 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0147153{col 26}{space 2} .0194515{col 37}{space 1} 0.76{col 46}{space 3}0.451{col 54}{space 4}-.0237367{col 67}{space 3} .0531673 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0117551{col 26}{space 2} .0221322{col 37}{space 1} 0.53{col 46}{space 3}0.596{col 54}{space 4} -.031996{col 67}{space 3} .0555062 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0006157{col 26}{space 2} .0251641{col 37}{space 1} 0.02{col 46}{space 3}0.981{col 54}{space 4} -.049129{col 67}{space 3} .0503603 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0065804{col 26}{space 2} .0242304{col 37}{space 1} 0.27{col 46}{space 3}0.786{col 54}{space 4}-.0413185{col 67}{space 3} .0544793 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0729238{col 26}{space 2} .0395401{col 37}{space 1} 1.84{col 46}{space 3}0.067{col 54}{space 4}-.0052395{col 67}{space 3} .1510871 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} -.027695{col 26}{space 2} .0340367{col 37}{space 1} -0.81{col 46}{space 3}0.417{col 54}{space 4}-.0949791{col 67}{space 3} .039589 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.4056129{col 26}{space 2} .0330129{col 37}{space 1} -12.29{col 46}{space 3}0.000{col 54}{space 4}-.4708731{col 67}{space 3}-.3403527 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.3563269{col 26}{space 2} .0381938{col 37}{space 1} -9.33{col 46}{space 3}0.000{col 54}{space 4}-.4318288{col 67}{space 3}-.2808249 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.3860662{col 26}{space 2} .0313379{col 37}{space 1} -12.32{col 46}{space 3}0.000{col 54}{space 4}-.4480153{col 67}{space 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1} -13.69{col 46}{space 3}0.000{col 54}{space 4}-.5151568{col 67}{space 3}-.3851376 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.3576674{col 26}{space 2} .0389771{col 37}{space 1} -9.18{col 46}{space 3}0.000{col 54}{space 4}-.4347176{col 67}{space 3}-.2806171 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.4745431{col 26}{space 2} .0209394{col 37}{space 1} -22.66{col 46}{space 3}0.000{col 54}{space 4}-.5159364{col 67}{space 3}-.4331497 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.3738103{col 26}{space 2} .0714941{col 37}{space 1} -5.23{col 46}{space 3}0.000{col 54}{space 4}-.5151405{col 67}{space 3}-.2324801 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5079024{col 26}{space 2} .0217168{col 37}{space 1} 23.39{col 46}{space 3}0.000{col 54}{space 4} .4649724{col 67}{space 3} .5508324 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(215 real changes made) {res}This Switching Regression took {com}22{res} seconds. {txt}(199 real changes made, 23 to missing) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 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26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00015 Average of the probability vector is: {txt}0.697 On iteration {com}50{txt} greatest diff is: {com}-0.032640 on f12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 171.26013 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.698 On iteration {com}100{txt} greatest diff is: {com}0.011150 on f2 in the{txt} second main {com}eqn {txt}Log-likelihood is : 171.79881 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.698 On iteration {com}150{txt} greatest diff is: {com}0.000338 on f2 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.06261 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.699 On iteration {com}200{txt} greatest diff is: {com}0.000297 on f5 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.2318 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00019 Average of the probability vector is: {txt}0.699 On iteration {com}250{txt} greatest diff is: {com}0.001110 on f11 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.35857 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.699 On iteration {com}300{txt} greatest diff is: {com}0.001470 on f5 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.54645 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.699 On iteration {com}350{txt} greatest diff is: {com}0.001978 on f5 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.6461 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.699 On iteration {com}400{txt} greatest diff is: {com}0.006282 on f5 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.71604 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.699 On iteration {com}450{txt} greatest diff is: {com}0.000248 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.76573 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.699 On iteration {com}500{txt} greatest diff is: {com}0.000216 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.80161 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.699 On iteration {com}550{txt} greatest diff is: {com}0.000189 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.8281 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.699 On iteration {com}600{txt} greatest diff is: {com}0.000166 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.84791 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}650{txt} greatest diff is: {com}0.000147 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.86299 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}700{txt} greatest diff is: {com}0.000153 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.87427 {res}This iteration took {com}0{res} second. On iter 750 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}750{txt} greatest diff is: {com}0.000123 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.88341 {res}This iteration took {com}0{res} second. On iter 800 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}800{txt} greatest diff is: {com}0.000128 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.88995 {res}This iteration took {com}0{res} second. On iter 850 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}850{txt} greatest diff is: {com}0.000126 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.89423 {res}This iteration took {com}0{res} second. On iter 900 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 On iteration {com}900{txt} greatest diff is: {com}0.000120 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 172.90093 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} .0000366 1.76e-06 {txt} 5% {res} .0019228 4.14e-06 {txt}10% {res} .1279233 .0000366 {txt}Obs {res} 214 {txt}25% {res} .3916283 .0002359 {txt}Sum of Wgt. {res} 214 {txt}50% {res} .880301 {txt}Mean {res} .6991463 {txt}Largest Std. Dev. {res} .3519482 {txt}75% {res} .9999398 1 {txt}90% {res} 1 1 {txt}Variance {res} .1238675 {txt}95% {res} 1 1 {txt}Skewness {res}-.7931287 {txt}99% {res} 1 1 {txt}Kurtosis {res} 2.071251 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 199 {txt}{hline 13}{c +}{hline 34} F(31, 167) = {res} 167.07 {txt} Model {c |} {res} 1461.46705 31 47.1440983 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 47.1233016 167 .282175459 {txt}R-squared ={res} 0.9688 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9630 {txt} Total {c |} {res} 1508.59035 198 7.61914318 {txt}Root MSE = {res} .5312 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 6}kz_cfs {c |}{col 14}{res}{space 2} 1.674167{col 26}{space 2} .0949968{col 37}{space 1} 17.62{col 46}{space 3}0.000{col 54}{space 4} 1.486618{col 67}{space 3} 1.861717 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0172466{col 26}{space 2} .0047859{col 37}{space 1} 3.60{col 46}{space 3}0.000{col 54}{space 4} .0077979{col 67}{space 3} .0266952 {txt}{space 8}kz_d {c |}{col 14}{res}{space 2} .9643615{col 26}{space 2} .3007695{col 37}{space 1} 3.21{col 46}{space 3}0.002{col 54}{space 4} .3705611{col 67}{space 3} 1.558162 {txt}{space 6}kz_div {c |}{col 14}{res}{space 2}-4.268588{col 26}{space 2} .4035166{col 37}{space 1} -10.58{col 46}{space 3}0.000{col 54}{space 4}-5.065239{col 67}{space 3}-3.471937 {txt}{space 7}kz_cs {c |}{col 14}{res}{space 2}-.6753864{col 26}{space 2} .2478139{col 37}{space 1} -2.73{col 46}{space 3}0.007{col 54}{space 4}-1.164638{col 67}{space 3}-.1861347 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 1.007293{col 26}{space 2} .2100489{col 37}{space 1} 4.80{col 46}{space 3}0.000{col 54}{space 4} .5925996{col 67}{space 3} 1.421986 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .6016719{col 26}{space 2} .207928{col 37}{space 1} 2.89{col 46}{space 3}0.004{col 54}{space 4} .1911658{col 67}{space 3} 1.012178 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.2002824{col 26}{space 2} .1862385{col 37}{space 1} -1.08{col 46}{space 3}0.284{col 54}{space 4}-.5679677{col 67}{space 3} .1674029 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.1648853{col 26}{space 2} .1895178{col 37}{space 1} -0.87{col 46}{space 3}0.386{col 54}{space 4}-.5390448{col 67}{space 3} .2092742 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .264135{col 26}{space 2} .1877895{col 37}{space 1} 1.41{col 46}{space 3}0.161{col 54}{space 4}-.1066124{col 67}{space 3} .6348824 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .6044441{col 26}{space 2} .1857007{col 37}{space 1} 3.25{col 46}{space 3}0.001{col 54}{space 4} .2378207{col 67}{space 3} .9710675 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .1858632{col 26}{space 2} .1790486{col 37}{space 1} 1.04{col 46}{space 3}0.301{col 54}{space 4}-.1676272{col 67}{space 3} .5393536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0255885{col 26}{space 2} .1869381{col 37}{space 1} 0.14{col 46}{space 3}0.891{col 54}{space 4} -.343478{col 67}{space 3} .394655 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .5954136{col 26}{space 2} .1851063{col 37}{space 1} 3.22{col 46}{space 3}0.002{col 54}{space 4} .2299635{col 67}{space 3} .9608636 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.8340824{col 26}{space 2} .1783969{col 37}{space 1} -4.68{col 46}{space 3}0.000{col 54}{space 4}-1.186286{col 67}{space 3}-.4818786 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.5892391{col 26}{space 2} .1767932{col 37}{space 1} -3.33{col 46}{space 3}0.001{col 54}{space 4}-.9382767{col 67}{space 3}-.2402015 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0754268{col 26}{space 2} .1825935{col 37}{space 1} 0.41{col 46}{space 3}0.680{col 54}{space 4}-.2850623{col 67}{space 3} .4359159 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-1.237467{col 26}{space 2} .1771675{col 37}{space 1} -6.98{col 46}{space 3}0.000{col 54}{space 4}-1.587244{col 67}{space 3}-.8876905 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-1.090053{col 26}{space 2} .1791466{col 37}{space 1} -6.08{col 46}{space 3}0.000{col 54}{space 4}-1.443737{col 67}{space 3} -.736369 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 5.026028{col 26}{space 2} .2416434{col 37}{space 1} 20.80{col 46}{space 3}0.000{col 54}{space 4} 4.548959{col 67}{space 3} 5.503098 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 2.306482{col 26}{space 2} .2389789{col 37}{space 1} 9.65{col 46}{space 3}0.000{col 54}{space 4} 1.834673{col 67}{space 3} 2.778291 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 2.212558{col 26}{space 2} .2256584{col 37}{space 1} 9.80{col 46}{space 3}0.000{col 54}{space 4} 1.767047{col 67}{space 3} 2.658069 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 5.997564{col 26}{space 2} .2314805{col 37}{space 1} 25.91{col 46}{space 3}0.000{col 54}{space 4} 5.540559{col 67}{space 3} 6.454569 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.2009972{col 26}{space 2} .1966208{col 37}{space 1} -1.02{col 46}{space 3}0.308{col 54}{space 4}-.5891799{col 67}{space 3} .1871855 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-1.675617{col 26}{space 2} .204643{col 37}{space 1} -8.19{col 46}{space 3}0.000{col 54}{space 4}-2.079638{col 67}{space 3}-1.271596 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 2.171926{col 26}{space 2} .2029916{col 37}{space 1} 10.70{col 46}{space 3}0.000{col 54}{space 4} 1.771166{col 67}{space 3} 2.572687 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 1.611955{col 26}{space 2} .2371944{col 37}{space 1} 6.80{col 46}{space 3}0.000{col 54}{space 4} 1.143669{col 67}{space 3} 2.080241 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 19.85577{col 26}{space 2} 1.138893{col 37}{space 1} 17.43{col 46}{space 3}0.000{col 54}{space 4} 17.60729{col 67}{space 3} 22.10425 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 1.214502{col 26}{space 2} .2060875{col 37}{space 1} 5.89{col 46}{space 3}0.000{col 54}{space 4} .8076298{col 67}{space 3} 1.621375 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} 8.99247{col 26}{space 2} .253024{col 37}{space 1} 35.54{col 46}{space 3}0.000{col 54}{space 4} 8.492932{col 67}{space 3} 9.492008 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 1.026755{col 26}{space 2} .2075825{col 37}{space 1} 4.95{col 46}{space 3}0.000{col 54}{space 4} .6169308{col 67}{space 3} 1.436579 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} -1.14432{col 26}{space 2} .1900489{col 37}{space 1} -6.02{col 46}{space 3}0.000{col 54}{space 4}-1.519528{col 67}{space 3}-.7691122 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 930 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.699 {res}First component regression {txt}(sum of wgt is 1.4948e+02) Linear regression Number of obs = {res} 204 {txt}{help j_robustsingular:F(30, 172) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9042 {txt}Root MSE = {res} .06364 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .0058292{col 26}{space 2} .0059869{col 37}{space 1} 0.97{col 46}{space 3}0.332{col 54}{space 4}-.0059881{col 67}{space 3} .0176465 {txt}{space 7}sales {c |}{col 14}{res}{space 2} -.003459{col 26}{space 2} .0029277{col 37}{space 1} -1.18{col 46}{space 3}0.239{col 54}{space 4}-.0092378{col 67}{space 3} .0023197 {txt}{space 4}profitab {c |}{col 14}{res}{space 2} -.004869{col 26}{space 2} .0074829{col 37}{space 1} -0.65{col 46}{space 3}0.516{col 54}{space 4}-.0196392{col 67}{space 3} .0099011 {txt}{space 7}tangi {c |}{col 14}{res}{space 2}-.0000773{col 26}{space 2} .0016593{col 37}{space 1} -0.05{col 46}{space 3}0.963{col 54}{space 4}-.0033524{col 67}{space 3} .0031978 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.0014341{col 26}{space 2} .000813{col 37}{space 1} -1.76{col 46}{space 3}0.080{col 54}{space 4}-.0030388{col 67}{space 3} .0001706 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0433509{col 26}{space 2} .0283359{col 37}{space 1} -1.53{col 46}{space 3}0.128{col 54}{space 4}-.0992818{col 67}{space 3} .0125801 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0375545{col 26}{space 2} .0240789{col 37}{space 1} -1.56{col 46}{space 3}0.121{col 54}{space 4}-.0850827{col 67}{space 3} .0099737 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0486203{col 26}{space 2} .019465{col 37}{space 1} -2.50{col 46}{space 3}0.013{col 54}{space 4}-.0870414{col 67}{space 3}-.0101992 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0525436{col 26}{space 2} .0217708{col 37}{space 1} -2.41{col 46}{space 3}0.017{col 54}{space 4} -.095516{col 67}{space 3}-.0095712 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0295957{col 26}{space 2} .01585{col 37}{space 1} -1.87{col 46}{space 3}0.064{col 54}{space 4}-.0608812{col 67}{space 3} .0016898 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0453585{col 26}{space 2} .0250264{col 37}{space 1} -1.81{col 46}{space 3}0.072{col 54}{space 4}-.0947569{col 67}{space 3} .00404 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0325376{col 26}{space 2} .0363871{col 37}{space 1} 0.89{col 46}{space 3}0.372{col 54}{space 4}-.0392851{col 67}{space 3} .1043603 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0061893{col 26}{space 2} .0166633{col 37}{space 1} -0.37{col 46}{space 3}0.711{col 54}{space 4}-.0390802{col 67}{space 3} .0267015 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0208687{col 26}{space 2} .0219898{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4}-.0225359{col 67}{space 3} .0642734 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0125386{col 26}{space 2} .0210198{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.0289513{col 67}{space 3} .0540285 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0033259{col 26}{space 2} .0238592{col 37}{space 1} -0.14{col 46}{space 3}0.889{col 54}{space 4}-.0504205{col 67}{space 3} .0437687 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .01394{col 26}{space 2} .026154{col 37}{space 1} 0.53{col 46}{space 3}0.595{col 54}{space 4}-.0376842{col 67}{space 3} .0655642 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0462231{col 26}{space 2} .0346713{col 37}{space 1} 1.33{col 46}{space 3}0.184{col 54}{space 4}-.0222129{col 67}{space 3} .114659 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0211683{col 26}{space 2} .0312022{col 37}{space 1} -0.68{col 46}{space 3}0.498{col 54}{space 4}-.0827569{col 67}{space 3} .0404202 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .0130439{col 26}{space 2} .0497038{col 37}{space 1} 0.26{col 46}{space 3}0.793{col 54}{space 4}-.0850641{col 67}{space 3} .1111519 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .0588694{col 26}{space 2} .053184{col 37}{space 1} 1.11{col 46}{space 3}0.270{col 54}{space 4}-.0461079{col 67}{space 3} .1638467 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .0204229{col 26}{space 2} .048674{col 37}{space 1} 0.42{col 46}{space 3}0.675{col 54}{space 4}-.0756524{col 67}{space 3} .1164983 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.0005314{col 26}{space 2} .0507466{col 37}{space 1} -0.01{col 46}{space 3}0.992{col 54}{space 4}-.1006977{col 67}{space 3} .0996349 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5245993{col 26}{space 2} .0724669{col 37}{space 1} 7.24{col 46}{space 3}0.000{col 54}{space 4} .3815603{col 67}{space 3} .6676384 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 1.777573{col 26}{space 2} .0555494{col 37}{space 1} 32.00{col 46}{space 3}0.000{col 54}{space 4} 1.667927{col 67}{space 3} 1.887219 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0571168{col 26}{space 2} .0413454{col 37}{space 1} -1.38{col 46}{space 3}0.169{col 54}{space 4}-.1387265{col 67}{space 3} .0244928 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1339014{col 26}{space 2} .0697416{col 37}{space 1} 1.92{col 46}{space 3}0.057{col 54}{space 4}-.0037583{col 67}{space 3} .271561 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0343163{col 26}{space 2} .0481739{col 37}{space 1} -0.71{col 46}{space 3}0.477{col 54}{space 4}-.1294045{col 67}{space 3} .0607719 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .0125181{col 26}{space 2} .0489344{col 37}{space 1} 0.26{col 46}{space 3}0.798{col 54}{space 4}-.0840713{col 67}{space 3} .1091075 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0495008{col 26}{space 2} .0439472{col 37}{space 1} -1.13{col 46}{space 3}0.262{col 54}{space 4}-.1362462{col 67}{space 3} .0372445 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .0213877{col 26}{space 2} .0760389{col 37}{space 1} 0.28{col 46}{space 3}0.779{col 54}{space 4}-.1287018{col 67}{space 3} .1714773 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0887002{col 26}{space 2} .0417144{col 37}{space 1} 2.13{col 46}{space 3}0.035{col 54}{space 4} .0063622{col 67}{space 3} .1710383 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 6.4516e+01) Linear regression Number of obs = {res} 164 {txt}{help j_robustsingular:F(29, 132) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9760 {txt}Root MSE = {res} .2574 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .9623576{col 26}{space 2} .0412044{col 37}{space 1} 23.36{col 46}{space 3}0.000{col 54}{space 4} .8808513{col 67}{space 3} 1.043864 {txt}{space 7}sales {c |}{col 14}{res}{space 2} .0522651{col 26}{space 2} .0195858{col 37}{space 1} 2.67{col 46}{space 3}0.009{col 54}{space 4} .0135225{col 67}{space 3} .0910078 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.2471523{col 26}{space 2} .0667292{col 37}{space 1} -3.70{col 46}{space 3}0.000{col 54}{space 4}-.3791493{col 67}{space 3}-.1151552 {txt}{space 7}tangi {c |}{col 14}{res}{space 2}-.0018581{col 26}{space 2} .0187517{col 37}{space 1} -0.10{col 46}{space 3}0.921{col 54}{space 4}-.0389509{col 67}{space 3} .0352347 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0387074{col 26}{space 2} .0050863{col 37}{space 1} 7.61{col 46}{space 3}0.000{col 54}{space 4} .0286463{col 67}{space 3} .0487685 {txt}{space 9}yr2 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54}{space 4}-.0371077{col 67}{space 3} .6646688 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .7989914{col 26}{space 2} .1326217{col 37}{space 1} 6.02{col 46}{space 3}0.000{col 54}{space 4} .5366526{col 67}{space 3} 1.06133 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.2270348{col 26}{space 2} .0877954{col 37}{space 1} -2.59{col 46}{space 3}0.011{col 54}{space 4}-.4007028{col 67}{space 3}-.0533667 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .4886832{col 26}{space 2} .2606952{col 37}{space 1} 1.87{col 46}{space 3}0.063{col 54}{space 4}-.0269976{col 67}{space 3} 1.004364 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0753174{col 26}{space 2} .1963913{col 37}{space 1} -0.38{col 46}{space 3}0.702{col 54}{space 4}-.4637989{col 67}{space 3} .313164 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2419539{col 26}{space 2} .0881705{col 37}{space 1} 2.74{col 46}{space 3}0.007{col 54}{space 4} .0675441{col 67}{space 3} .4163638 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0904341{col 26}{space 2} .1434051{col 37}{space 1} -0.63{col 46}{space 3}0.529{col 54}{space 4}-.3741036{col 67}{space 3} .1932354 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .1497533{col 26}{space 2} .1191361{col 37}{space 1} 1.26{col 46}{space 3}0.211{col 54}{space 4}-.0859096{col 67}{space 3} .3854163 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0392211{col 26}{space 2} .1108429{col 37}{space 1} -0.35{col 46}{space 3}0.724{col 54}{space 4}-.2584793{col 67}{space 3} .180037 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .5223694{col 26}{space 2} .3002626{col 37}{space 1} 1.74{col 46}{space 3}0.084{col 54}{space 4}-.0715798{col 67}{space 3} 1.116319 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.4745593{col 26}{space 2} .2513862{col 37}{space 1} -1.89{col 46}{space 3}0.061{col 54}{space 4}-.9718261{col 67}{space 3} .0227075 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.9310886{col 26}{space 2} .2163128{col 37}{space 1} -4.30{col 46}{space 3}0.000{col 54}{space 4}-1.358977{col 67}{space 3}-.5032005 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 1.291075{col 26}{space 2} .2203139{col 37}{space 1} 5.86{col 46}{space 3}0.000{col 54}{space 4} .8552729{col 67}{space 3} 1.726878 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1942596{col 26}{space 2} .1806797{col 37}{space 1} -1.08{col 46}{space 3}0.284{col 54}{space 4}-.5516619{col 67}{space 3} .1631427 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1637714{col 26}{space 2} .1205112{col 37}{space 1} -1.36{col 46}{space 3}0.176{col 54}{space 4}-.4021545{col 67}{space 3} .0746116 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1078239{col 26}{space 2} .1162017{col 37}{space 1} -0.93{col 46}{space 3}0.355{col 54}{space 4}-.3376823{col 67}{space 3} .1220344 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 2.316775{col 26}{space 2} .2067151{col 37}{space 1} 11.21{col 46}{space 3}0.000{col 54}{space 4} 1.907872{col 67}{space 3} 2.725678 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 1.109804{col 26}{space 2} .1410361{col 37}{space 1} 7.87{col 46}{space 3}0.000{col 54}{space 4} .8308209{col 67}{space 3} 1.388788 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1474501{col 26}{space 2} .1345262{col 37}{space 1} -1.10{col 46}{space 3}0.275{col 54}{space 4}-.4135562{col 67}{space 3} .1186559 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -.468992{col 26}{space 2} .1083548{col 37}{space 1} -4.33{col 46}{space 3}0.000{col 54}{space 4}-.6833284{col 67}{space 3}-.2546555 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} -.576176{col 26}{space 2} .3657879{col 37}{space 1} -1.58{col 46}{space 3}0.118{col 54}{space 4}-1.299741{col 67}{space 3} .1473886 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2740001{col 26}{space 2} .1161381{col 37}{space 1} -2.36{col 46}{space 3}0.020{col 54}{space 4}-.5037327{col 67}{space 3}-.0442675 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(221 real changes made) {res}This Switching Regression took {com}40{res} seconds. {txt}(0 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 222 {txt}{hline 13}{c +}{hline 34} F(31, 190) = {res} 12.49 {txt} Model {c |} {res} 141.553335 31 4.5662366 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 69.438316 190 .365464821 {txt}R-squared ={res} 0.6709 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.6172 {txt} Total {c |} {res} 210.991651 221 .954713351 {txt}Root MSE = {res} .60454 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .4821327{col 26}{space 2} .048169{col 37}{space 1} 10.01{col 46}{space 3}0.000{col 54}{space 4} .3871181{col 67}{space 3} .5771474 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0285433{col 26}{space 2} .0202815{col 37}{space 1} -1.41{col 46}{space 3}0.161{col 54}{space 4}-.0685492{col 67}{space 3} .0114625 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0289215{col 26}{space 2} .0583177{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4}-.1439549{col 67}{space 3} .0861118 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .034121{col 26}{space 2} .0176772{col 37}{space 1} 1.93{col 46}{space 3}0.055{col 54}{space 4}-.0007478{col 67}{space 3} .0689898 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0077474{col 26}{space 2} .0062522{col 37}{space 1} 1.24{col 46}{space 3}0.217{col 54}{space 4}-.0045852{col 67}{space 3} .02008 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0832264{col 26}{space 2} .2037061{col 37}{space 1} 0.41{col 46}{space 3}0.683{col 54}{space 4}-.3185896{col 67}{space 3} .4850424 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -.126962{col 26}{space 2} .2032728{col 37}{space 1} -0.62{col 46}{space 3}0.533{col 54}{space 4}-.5279234{col 67}{space 3} .2739994 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0733523{col 26}{space 2} .2055788{col 37}{space 1} 0.36{col 46}{space 3}0.722{col 54}{space 4}-.3321576{col 67}{space 3} .4788623 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2544853{col 26}{space 2} .2087599{col 37}{space 1} -1.22{col 46}{space 3}0.224{col 54}{space 4} -.66627{col 67}{space 3} .1572994 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0059613{col 26}{space 2} .2019919{col 37}{space 1} -0.03{col 46}{space 3}0.976{col 54}{space 4} -.404396{col 67}{space 3} .3924733 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0097956{col 26}{space 2} .1982906{col 37}{space 1} -0.05{col 46}{space 3}0.961{col 54}{space 4}-.4009293{col 67}{space 3} .3813382 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .2167708{col 26}{space 2} .1976057{col 37}{space 1} 1.10{col 46}{space 3}0.274{col 54}{space 4}-.1730119{col 67}{space 3} .6065536 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.1537051{col 26}{space 2} .1971893{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.5426666{col 67}{space 3} .2352564 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0596839{col 26}{space 2} .1954351{col 37}{space 1} 0.31{col 46}{space 3}0.760{col 54}{space 4}-.3258174{col 67}{space 3} .4451852 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0358223{col 26}{space 2} .1980381{col 37}{space 1} 0.18{col 46}{space 3}0.857{col 54}{space 4}-.3548133{col 67}{space 3} .426458 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2612704{col 26}{space 2} .1923846{col 37}{space 1} 1.36{col 46}{space 3}0.176{col 54}{space 4}-.1182137{col 67}{space 3} .6407544 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .172893{col 26}{space 2} .1930309{col 37}{space 1} 0.90{col 46}{space 3}0.372{col 54}{space 4}-.2078659{col 67}{space 3} .5536519 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5594083{col 26}{space 2} .1951901{col 37}{space 1} 2.87{col 46}{space 3}0.005{col 54}{space 4} .1743904{col 67}{space 3} .9444262 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .2361468{col 26}{space 2} .1916733{col 37}{space 1} 1.23{col 46}{space 3}0.219{col 54}{space 4}-.1419342{col 67}{space 3} .6142278 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.5144942{col 26}{space 2} .2690564{col 37}{space 1} -1.91{col 46}{space 3}0.057{col 54}{space 4}-1.045216{col 67}{space 3} .0162271 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2075881{col 26}{space 2} .2547827{col 37}{space 1} -0.81{col 46}{space 3}0.416{col 54}{space 4}-.7101541{col 67}{space 3} .294978 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} -.05767{col 26}{space 2} .2453065{col 37}{space 1} -0.24{col 46}{space 3}0.814{col 54}{space 4}-.5415441{col 67}{space 3} .426204 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3639647{col 26}{space 2} .2845836{col 37}{space 1} -1.28{col 46}{space 3}0.202{col 54}{space 4} -.925314{col 67}{space 3} .1973845 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5509781{col 26}{space 2} .2580827{col 37}{space 1} 2.13{col 46}{space 3}0.034{col 54}{space 4} .0419028{col 67}{space 3} 1.060053 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .9201589{col 26}{space 2} .2209305{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4843673{col 67}{space 3} 1.355951 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1466754{col 26}{space 2} .2139735{col 37}{space 1} -0.69{col 46}{space 3}0.494{col 54}{space 4}-.5687442{col 67}{space 3} .2753933 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1586671{col 26}{space 2} .2660441{col 37}{space 1} 0.60{col 46}{space 3}0.552{col 54}{space 4}-.3661125{col 67}{space 3} .6834466 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .004355{col 26}{space 2} .2482915{col 37}{space 1} 0.02{col 46}{space 3}0.986{col 54}{space 4}-.4854069{col 67}{space 3} .4941169 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0797787{col 26}{space 2} .2094118{col 37}{space 1} -0.38{col 46}{space 3}0.704{col 54}{space 4}-.4928494{col 67}{space 3} .333292 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0944817{col 26}{space 2} .2048784{col 37}{space 1} -0.46{col 46}{space 3}0.645{col 54}{space 4} -.49861{col 67}{space 3} .3096467 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .3132846{col 26}{space 2} .4314135{col 37}{space 1} 0.73{col 46}{space 3}0.469{col 54}{space 4}-.5376908{col 67}{space 3} 1.16426 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .013264{col 26}{space 2} .174783{col 37}{space 1} 0.08{col 46}{space 3}0.940{col 54}{space 4}-.3315004{col 67}{space 3} .3580283 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00031 Average of the probability vector is: {txt}0.302 On iteration {com}50{txt} greatest diff is: {com}0.074480 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 171.81241 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.300 On iteration {com}100{txt} greatest diff is: {com}0.007007 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.21559 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.300 On iteration {com}150{txt} greatest diff is: {com}-0.017875 on f12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.30843 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.300 On iteration {com}200{txt} greatest diff is: {com}0.001202 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.35034 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.300 On iteration {com}250{txt} greatest diff is: {com}0.000507 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.36591 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.300 On iteration {com}300{txt} greatest diff is: {com}0.000285 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.37526 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.300 On iteration {com}350{txt} greatest diff is: {com}0.000185 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.38149 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.300 On iteration {com}400{txt} greatest diff is: {com}0.000130 on yr15 in the{txt} first main {com}eqn {txt}Log-likelihood is : 172.38596 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 4.89e-13 2.44e-17 {txt} 5% {res} 1.86e-08 2.00e-13 {txt}10% {res} 5.15e-06 4.89e-13 {txt}Obs {res} 206 {txt}25% {res} .000622 6.45e-11 {txt}Sum of Wgt. {res} 206 {txt}50% {res} .1118344 {txt}Mean {res} .2997965 {txt}Largest Std. Dev. {res} .3511955 {txt}75% {res} .6242631 .9994973 {txt}90% {res} .9107353 .9997024 {txt}Variance {res} .1233383 {txt}95% {res} .9866461 .9999937 {txt}Skewness {res} .8353673 {txt}99% {res} .9997024 .9999967 {txt}Kurtosis {res} 2.167076 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 206 {txt}{hline 13}{c +}{hline 34} F(32, 173) = {res} 130.34 {txt} Model {c |} {res} 1165.09403 32 36.4091885 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 48.3242465 173 .279330904 {txt}R-squared ={res} 0.9602 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9528 {txt} Total {c |} {res} 1213.41828 205 5.91911355 {txt}Root MSE = {res} .52852 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 8}ww_d {c |}{col 14}{res}{space 2}-1.560165{col 26}{space 2} .3011174{col 37}{space 1} -5.18{col 46}{space 3}0.000{col 54}{space 4}-2.154501{col 67}{space 3}-.9658277 {txt}{space 6}ww_div {c |}{col 14}{res}{space 2} -.643362{col 26}{space 2} .1392909{col 37}{space 1} -4.62{col 46}{space 3}0.000{col 54}{space 4}-.9182904{col 67}{space 3}-.3684337 {txt}{space 3}ww_gsales {c |}{col 14}{res}{space 2}-.9678606{col 26}{space 2} .185424{col 37}{space 1} -5.22{col 46}{space 3}0.000{col 54}{space 4}-1.333845{col 67}{space 3} -.601876 {txt}{space 5}ww_size {c |}{col 14}{res}{space 2}-.9417782{col 26}{space 2} .1078387{col 37}{space 1} -8.73{col 46}{space 3}0.000{col 54}{space 4}-1.154627{col 67}{space 3}-.7289291 {txt}{space 7}ww_cs {c |}{col 14}{res}{space 2} 3.311327{col 26}{space 2} .3801462{col 37}{space 1} 8.71{col 46}{space 3}0.000{col 54}{space 4} 2.561005{col 67}{space 3} 4.061648 {txt}{space 6}ww_cfs {c |}{col 14}{res}{space 2}-2.768341{col 26}{space 2} .3128469{col 37}{space 1} -8.85{col 46}{space 3}0.000{col 54}{space 4}-3.385829{col 67}{space 3}-2.150853 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-2.652171{col 26}{space 2} .2111798{col 37}{space 1} -12.56{col 46}{space 3}0.000{col 54}{space 4}-3.068992{col 67}{space 3} -2.23535 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} -1.31971{col 26}{space 2} .1876434{col 37}{space 1} -7.03{col 46}{space 3}0.000{col 54}{space 4}-1.690075{col 67}{space 3}-.9493443 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.390017{col 26}{space 2} .180652{col 37}{space 1} -2.16{col 46}{space 3}0.032{col 54}{space 4}-.7465827{col 67}{space 3}-.0334513 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.2727017{col 26}{space 2} .1882598{col 37}{space 1} -1.45{col 46}{space 3}0.149{col 54}{space 4}-.6442834{col 67}{space 3} .0988801 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.7281799{col 26}{space 2} .192259{col 37}{space 1} -3.79{col 46}{space 3}0.000{col 54}{space 4}-1.107655{col 67}{space 3}-.3487047 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-1.699885{col 26}{space 2} .1894196{col 37}{space 1} -8.97{col 46}{space 3}0.000{col 54}{space 4}-2.073756{col 67}{space 3}-1.326015 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-.5814561{col 26}{space 2} .1809762{col 37}{space 1} -3.21{col 46}{space 3}0.002{col 54}{space 4}-.9386616{col 67}{space 3}-.2242505 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.5220688{col 26}{space 2} .1844772{col 37}{space 1} -2.83{col 46}{space 3}0.005{col 54}{space 4}-.8861846{col 67}{space 3} -.157953 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.6768629{col 26}{space 2} .177434{col 37}{space 1} -3.81{col 46}{space 3}0.000{col 54}{space 4}-1.027077{col 67}{space 3}-.3266488 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .214319{col 26}{space 2} .1788155{col 37}{space 1} 1.20{col 46}{space 3}0.232{col 54}{space 4}-.1386219{col 67}{space 3} .5672599 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0027304{col 26}{space 2} .1743188{col 37}{space 1} 0.02{col 46}{space 3}0.988{col 54}{space 4} -.341335{col 67}{space 3} .3467958 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.4211417{col 26}{space 2} .1770253{col 37}{space 1} -2.38{col 46}{space 3}0.018{col 54}{space 4}-.7705492{col 67}{space 3}-.0717343 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .8579016{col 26}{space 2} .1763089{col 37}{space 1} 4.87{col 46}{space 3}0.000{col 54}{space 4} .5099081{col 67}{space 3} 1.205895 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .6318634{col 26}{space 2} .17403{col 37}{space 1} 3.63{col 46}{space 3}0.000{col 54}{space 4} .288368{col 67}{space 3} .9753589 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-7.189933{col 26}{space 2} .2572054{col 37}{space 1} -27.95{col 46}{space 3}0.000{col 54}{space 4}-7.697597{col 67}{space 3}-6.682268 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-1.980433{col 26}{space 2} .2668389{col 37}{space 1} -7.42{col 46}{space 3}0.000{col 54}{space 4}-2.507112{col 67}{space 3}-1.453754 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-1.504093{col 26}{space 2} .2252248{col 37}{space 1} -6.68{col 46}{space 3}0.000{col 54}{space 4}-1.948636{col 67}{space 3}-1.059551 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-6.390604{col 26}{space 2} .2747195{col 37}{space 1} -23.26{col 46}{space 3}0.000{col 54}{space 4}-6.932838{col 67}{space 3}-5.848371 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-1.746129{col 26}{space 2} .2464595{col 37}{space 1} -7.08{col 46}{space 3}0.000{col 54}{space 4}-2.232584{col 67}{space 3}-1.259674 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .3897468{col 26}{space 2} .2376388{col 37}{space 1} 1.64{col 46}{space 3}0.103{col 54}{space 4}-.0792977{col 67}{space 3} .8587914 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-2.801527{col 26}{space 2} .2129205{col 37}{space 1} -13.16{col 46}{space 3}0.000{col 54}{space 4}-3.221784{col 67}{space 3}-2.381271 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.3797019{col 26}{space 2} .268666{col 37}{space 1} -1.41{col 46}{space 3}0.159{col 54}{space 4}-.9099871{col 67}{space 3} .1505833 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-7.112938{col 26}{space 2} .337273{col 37}{space 1} -21.09{col 46}{space 3}0.000{col 54}{space 4}-7.778638{col 67}{space 3}-6.447238 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.6470623{col 26}{space 2} .2240158{col 37}{space 1} -2.89{col 46}{space 3}0.004{col 54}{space 4}-1.089218{col 67}{space 3}-.2049063 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-7.502746{col 26}{space 2} .2281936{col 37}{space 1} -32.88{col 46}{space 3}0.000{col 54}{space 4}-7.953148{col 67}{space 3}-7.052344 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-2.633358{col 26}{space 2} .2214442{col 37}{space 1} -11.89{col 46}{space 3}0.000{col 54}{space 4}-3.070438{col 67}{space 3}-2.196278 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} 13.45039{col 26}{space 2} 1.22495{col 37}{space 1} 10.98{col 46}{space 3}0.000{col 54}{space 4} 11.03262{col 67}{space 3} 15.86816 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 446 the mean absolute change in the probability vector is : 0.00000 Average of the probability vector is: {txt}0.300 {res}First component regression {txt}(sum of wgt is 6.2076e+01) Linear regression Number of obs = {res} 206 {txt}F(31, 174) = {res} 57087.42 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.9816 {txt}Root MSE = {res} .22206 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .9895476{col 26}{space 2} .0415267{col 37}{space 1} 23.83{col 46}{space 3}0.000{col 54}{space 4} .9075867{col 67}{space 3} 1.071509 {txt}{space 7}sales {c |}{col 14}{res}{space 2} .0365918{col 26}{space 2} .0156777{col 37}{space 1} 2.33{col 46}{space 3}0.021{col 54}{space 4} .0056488{col 67}{space 3} .0675348 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.1473281{col 26}{space 2} .0520571{col 37}{space 1} -2.83{col 46}{space 3}0.005{col 54}{space 4}-.2500727{col 67}{space 3}-.0445835 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} -.019712{col 26}{space 2} .0176292{col 37}{space 1} -1.12{col 46}{space 3}0.265{col 54}{space 4}-.0545067{col 67}{space 3} .0150826 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} .0295721{col 26}{space 2} .0039253{col 37}{space 1} 7.53{col 46}{space 3}0.000{col 54}{space 4} .0218249{col 67}{space 3} .0373194 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .2520807{col 26}{space 2} .1331219{col 37}{space 1} 1.89{col 46}{space 3}0.060{col 54}{space 4}-.0106609{col 67}{space 3} .5148223 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .4048429{col 26}{space 2} .4222241{col 37}{space 1} 0.96{col 46}{space 3}0.339{col 54}{space 4}-.4284972{col 67}{space 3} 1.238183 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0480435{col 26}{space 2} .1204028{col 37}{space 1} 0.40{col 46}{space 3}0.690{col 54}{space 4}-.1895944{col 67}{space 3} .2856814 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .0359151{col 26}{space 2} .0943186{col 37}{space 1} 0.38{col 46}{space 3}0.704{col 54}{space 4}-.1502408{col 67}{space 3} .222071 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.082978{col 26}{space 2} .1340407{col 37}{space 1} -0.62{col 46}{space 3}0.537{col 54}{space 4} -.347533{col 67}{space 3} .1815771 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .1030736{col 26}{space 2} .211658{col 37}{space 1} 0.49{col 46}{space 3}0.627{col 54}{space 4} -.314674{col 67}{space 3} .5208211 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .9019639{col 26}{space 2} .1235063{col 37}{space 1} 7.30{col 46}{space 3}0.000{col 54}{space 4} .6582006{col 67}{space 3} 1.145727 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.2090956{col 26}{space 2} .1341473{col 37}{space 1} -1.56{col 46}{space 3}0.121{col 54}{space 4}-.4738609{col 67}{space 3} .0556697 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .2661645{col 26}{space 2} .201366{col 37}{space 1} 1.32{col 46}{space 3}0.188{col 54}{space 4}-.1312698{col 67}{space 3} .6635988 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.1266667{col 26}{space 2} .1658711{col 37}{space 1} -0.76{col 46}{space 3}0.446{col 54}{space 4}-.4540452{col 67}{space 3} .2007117 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .1362427{col 26}{space 2} .0885483{col 37}{space 1} 1.54{col 46}{space 3}0.126{col 54}{space 4}-.0385243{col 67}{space 3} .3110098 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0967753{col 26}{space 2} .1087356{col 37}{space 1} -0.89{col 46}{space 3}0.375{col 54}{space 4}-.3113857{col 67}{space 3} .1178352 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .1526666{col 26}{space 2} .1075628{col 37}{space 1} 1.42{col 46}{space 3}0.158{col 54}{space 4}-.0596291{col 67}{space 3} .3649623 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0062851{col 26}{space 2} .114301{col 37}{space 1} 0.05{col 46}{space 3}0.956{col 54}{space 4}-.2193099{col 67}{space 3} .2318801 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .8609983{col 26}{space 2} .2785302{col 37}{space 1} 3.09{col 46}{space 3}0.002{col 54}{space 4} .3112657{col 67}{space 3} 1.410731 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1018283{col 26}{space 2} .2731246{col 37}{space 1} -0.37{col 46}{space 3}0.710{col 54}{space 4} -.640892{col 67}{space 3} .4372355 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.8085519{col 26}{space 2} .1865038{col 37}{space 1} -4.34{col 46}{space 3}0.000{col 54}{space 4}-1.176653{col 67}{space 3}-.4404509 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .5900148{col 26}{space 2} .2873628{col 37}{space 1} 2.05{col 46}{space 3}0.042{col 54}{space 4} .0228493{col 67}{space 3} 1.15718 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0952204{col 26}{space 2} .1535839{col 37}{space 1} -0.62{col 46}{space 3}0.536{col 54}{space 4}-.3983476{col 67}{space 3} .2079068 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.0518864{col 26}{space 2} .1250672{col 37}{space 1} -0.41{col 46}{space 3}0.679{col 54}{space 4}-.2987304{col 67}{space 3} .1949577 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} -.048861{col 26}{space 2} .1144677{col 37}{space 1} -0.43{col 46}{space 3}0.670{col 54}{space 4} -.274785{col 67}{space 3} .1770629 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 2.540603{col 26}{space 2} .2029615{col 37}{space 1} 12.52{col 46}{space 3}0.000{col 54}{space 4} 2.14002{col 67}{space 3} 2.941186 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 1.206057{col 26}{space 2} .1302323{col 37}{space 1} 9.26{col 46}{space 3}0.000{col 54}{space 4} .9490189{col 67}{space 3} 1.463096 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1869422{col 26}{space 2} .1195563{col 37}{space 1} -1.56{col 46}{space 3}0.120{col 54}{space 4}-.4229095{col 67}{space 3} .049025 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.4926263{col 26}{space 2} .1103848{col 37}{space 1} -4.46{col 46}{space 3}0.000{col 54}{space 4}-.7104919{col 67}{space 3}-.2747607 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.3017378{col 26}{space 2} .2523366{col 37}{space 1} -1.20{col 46}{space 3}0.233{col 54}{space 4}-.7997723{col 67}{space 3} .1962968 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1111203{col 26}{space 2} .1119804{col 37}{space 1} -0.99{col 46}{space 3}0.322{col 54}{space 4} -.332135{col 67}{space 3} .1098944 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 1.4392e+02) Linear regression Number of obs = {res} 172 {txt}{help j_robustsingular:F(30, 140) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9145 {txt}Root MSE = {res} .06227 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} delta_D{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 4}fdeficit {c |}{col 14}{res}{space 2} .0043393{col 26}{space 2} .0058177{col 37}{space 1} 0.75{col 46}{space 3}0.457{col 54}{space 4}-.0071625{col 67}{space 3} .0158412 {txt}{space 7}sales {c |}{col 14}{res}{space 2}-.0024319{col 26}{space 2} .0028684{col 37}{space 1} -0.85{col 46}{space 3}0.398{col 54}{space 4} -.008103{col 67}{space 3} .0032392 {txt}{space 4}profitab {c |}{col 14}{res}{space 2}-.0073912{col 26}{space 2} .0074663{col 37}{space 1} -0.99{col 46}{space 3}0.324{col 54}{space 4}-.0221524{col 67}{space 3} .00737 {txt}{space 7}tangi {c |}{col 14}{res}{space 2} .0004853{col 26}{space 2} .0016464{col 37}{space 1} 0.29{col 46}{space 3}0.769{col 54}{space 4}-.0027696{col 67}{space 3} .0037403 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.0016492{col 26}{space 2} .0008989{col 37}{space 1} -1.83{col 46}{space 3}0.069{col 54}{space 4}-.0034264{col 67}{space 3} .0001279 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0368118{col 26}{space 2} .0263352{col 37}{space 1} -1.40{col 46}{space 3}0.164{col 54}{space 4}-.0888779{col 67}{space 3} .0152544 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0430403{col 26}{space 2} .0239862{col 37}{space 1} -1.79{col 46}{space 3}0.075{col 54}{space 4}-.0904623{col 67}{space 3} .0043817 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0505809{col 26}{space 2} .0216156{col 37}{space 1} -2.34{col 46}{space 3}0.021{col 54}{space 4}-.0933161{col 67}{space 3}-.0078458 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0545728{col 26}{space 2} .0248073{col 37}{space 1} -2.20{col 46}{space 3}0.029{col 54}{space 4}-.1036182{col 67}{space 3}-.0055275 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0256744{col 26}{space 2} .0160635{col 37}{space 1} -1.60{col 46}{space 3}0.112{col 54}{space 4}-.0574329{col 67}{space 3} .006084 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0446848{col 26}{space 2} .0255588{col 37}{space 1} -1.75{col 46}{space 3}0.083{col 54}{space 4} -.095216{col 67}{space 3} .0058463 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0273354{col 26}{space 2} .0346386{col 37}{space 1} 0.79{col 46}{space 3}0.431{col 54}{space 4}-.0411469{col 67}{space 3} .0958176 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0060363{col 26}{space 2} .0169072{col 37}{space 1} -0.36{col 46}{space 3}0.722{col 54}{space 4}-.0394627{col 67}{space 3} .0273901 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0058646{col 26}{space 2} .018913{col 37}{space 1} 0.31{col 46}{space 3}0.757{col 54}{space 4}-.0315273{col 67}{space 3} .0432566 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0096582{col 26}{space 2} .0214354{col 37}{space 1} 0.45{col 46}{space 3}0.653{col 54}{space 4}-.0327207{col 67}{space 3} .0520371 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0064805{col 26}{space 2} .0233171{col 37}{space 1} -0.28{col 46}{space 3}0.781{col 54}{space 4}-.0525797{col 67}{space 3} .0396188 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0118931{col 26}{space 2} .0273259{col 37}{space 1} 0.44{col 46}{space 3}0.664{col 54}{space 4}-.0421317{col 67}{space 3} .0659179 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .04599{col 26}{space 2} .0364256{col 37}{space 1} 1.26{col 46}{space 3}0.209{col 54}{space 4}-.0260253{col 67}{space 3} .1180054 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0233604{col 26}{space 2} .0327937{col 37}{space 1} -0.71{col 46}{space 3}0.477{col 54}{space 4}-.0881953{col 67}{space 3} .0414746 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} .016042{col 26}{space 2} .056579{col 37}{space 1} 0.28{col 46}{space 3}0.777{col 54}{space 4}-.0958177{col 67}{space 3} .1279018 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} .0589661{col 26}{space 2} .0613322{col 37}{space 1} 0.96{col 46}{space 3}0.338{col 54}{space 4}-.0622909{col 67}{space 3} .180223 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} .0293417{col 26}{space 2} .0561883{col 37}{space 1} 0.52{col 46}{space 3}0.602{col 54}{space 4}-.0817455{col 67}{space 3} .1404289 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .0094719{col 26}{space 2} .0568909{col 37}{space 1} 0.17{col 46}{space 3}0.868{col 54}{space 4}-.1030044{col 67}{space 3} .1219483 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5207687{col 26}{space 2} .078213{col 37}{space 1} 6.66{col 46}{space 3}0.000{col 54}{space 4} .3661374{col 67}{space 3} .6754 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 1.790723{col 26}{space 2} .0599555{col 37}{space 1} 29.87{col 46}{space 3}0.000{col 54}{space 4} 1.672188{col 67}{space 3} 1.909258 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0531721{col 26}{space 2} .0471572{col 37}{space 1} -1.13{col 46}{space 3}0.261{col 54}{space 4}-.1464044{col 67}{space 3} .0400602 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .1499232{col 26}{space 2} .0779287{col 37}{space 1} 1.92{col 46}{space 3}0.056{col 54}{space 4}-.0041461{col 67}{space 3} .3039924 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0385296{col 26}{space 2} .0529312{col 37}{space 1} -0.73{col 46}{space 3}0.468{col 54}{space 4}-.1431775{col 67}{space 3} .0661182 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0043999{col 26}{space 2} .0515784{col 37}{space 1} -0.09{col 46}{space 3}0.932{col 54}{space 4}-.1063732{col 67}{space 3} .0975735 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0431329{col 26}{space 2} .0503522{col 37}{space 1} -0.86{col 46}{space 3}0.393{col 54}{space 4}-.1426819{col 67}{space 3} .056416 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.0006845{col 26}{space 2} .0789149{col 37}{space 1} -0.01{col 46}{space 3}0.993{col 54}{space 4}-.1567035{col 67}{space 3} .1553345 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0825674{col 26}{space 2} .0489588{col 37}{space 1} 1.69{col 46}{space 3}0.094{col 54}{space 4}-.0142268{col 67}{space 3} .1793617 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(206 real changes made) {res}This Switching Regression took {com}20{res} seconds. {txt}(0 real changes made) {com}. local regime hpf kzf wwf {txt} {com}. foreach l of local regime {c -(} {txt} 2{com}. switchr inv `l' {txt} 3{com}. gen byte inv`l'=fc>0.5 //storing classification results in binary {txt} 4{com}. replace fc=fcba //reseting the initial values {txt} 5{com}. {c )-} {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00397 Average of the probability vector is: {txt}0.507 On iteration {com}50{txt} greatest diff is: {com}0.118217 on yr2 in the{txt} first main {com}eqn {txt}Log-likelihood is : 295.15053 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00062 Average of the probability vector is: {txt}0.490 On iteration {com}100{txt} greatest diff is: {com}0.202146 on yr11 in the{txt} first main {com}eqn {txt}Log-likelihood is : 291.22392 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00035 Average of the probability vector is: {txt}0.483 On iteration {com}150{txt} greatest diff is: {com}-0.003538 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 289.83796 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00027 Average of the probability vector is: {txt}0.478 On iteration {com}200{txt} greatest diff is: {com}-0.003736 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 289.97026 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00019 Average of the probability vector is: {txt}0.475 On iteration {com}250{txt} greatest diff is: {com}0.003778 on yr13 in the{txt} first main {com}eqn {txt}Log-likelihood is : 290.6471 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00016 Average of the probability vector is: {txt}0.473 On iteration {com}300{txt} greatest diff is: {com}0.004617 on yr13 in the{txt} first main {com}eqn {txt}Log-likelihood is : 291.23162 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00013 Average of the probability vector is: {txt}0.470 On iteration {com}350{txt} greatest diff is: {com}0.004780 on yr13 in the{txt} first main {com}eqn {txt}Log-likelihood is : 291.65752 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00012 Average of the probability vector is: {txt}0.468 On iteration {com}400{txt} greatest diff is: {com}0.004584 on yr13 in the{txt} first main {com}eqn {txt}Log-likelihood is : 291.91367 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00009 Average of the probability vector is: {txt}0.466 On iteration {com}450{txt} greatest diff is: {com}0.002338 on yr13 in the{txt} first main {com}eqn {txt}Log-likelihood is : 292.05109 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.465 On iteration {com}500{txt} greatest diff is: {com}-0.000846 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.16734 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.464 On iteration {com}550{txt} greatest diff is: {com}-0.000537 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.29678 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.464 On iteration {com}600{txt} greatest diff is: {com}-0.000522 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.45198 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.464 On iteration {com}650{txt} greatest diff is: {com}-0.000603 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.58928 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.464 On iteration {com}700{txt} greatest diff is: {com}-0.000864 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.7021 {res}This iteration took {com}0{res} second. On iter 750 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.464 On iteration {com}750{txt} greatest diff is: {com}0.003226 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 292.83454 {res}This iteration took {com}0{res} second. On iter 800 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.469 On iteration {com}800{txt} greatest diff is: {com}0.002498 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 293.7513 {res}This iteration took {com}0{res} second. On iter 850 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.473 On iteration {com}850{txt} greatest diff is: {com}0.000290 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 294.84561 {res}This iteration took {com}0{res} second. On iter 900 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.474 On iteration {com}900{txt} greatest diff is: {com}-0.000276 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 295.14808 {res}This iteration took {com}0{res} second. On iter 950 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.474 On iteration {com}950{txt} greatest diff is: {com}-0.000230 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 295.332 {res}This iteration took {com}0{res} second. On iter 1000 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.474 On iteration {com}1000{txt} greatest diff is: {com}-0.000187 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 295.48696 {res}This iteration took {com}0{res} second. On iter 1050 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.474 On iteration {com}1050{txt} greatest diff is: {com}-0.000146 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 295.62087 {res}This iteration took {com}0{res} second. On iter 1100 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.474 On iteration {com}1100{txt} greatest diff is: {com}-0.000115 on yr15 in the{txt} second main {com}eqn {txt}Log-likelihood is : 295.73928 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 2.1e-183 1.2e-216 {txt} 5% {res} 5.1e-123 2.1e-183 {txt}10% {res} 2.26e-60 1.1e-178 {txt}Obs {res} 189 {txt}25% {res} 7.57e-14 4.6e-159 {txt}Sum of Wgt. {res} 189 {txt}50% {res} .15361 {txt}Mean {res} .4737257 {txt}Largest Std. Dev. {res} .4808933 {txt}75% {res} 1 1 {txt}90% {res} 1 1 {txt}Variance {res} .2312584 {txt}95% {res} 1 1 {txt}Skewness {res} .1085433 {txt}99% {res} 1 1 {txt}Kurtosis {res} 1.055514 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 154 {txt}{hline 13}{c +}{hline 34} F(29, 124) = {res} 21547.53 {txt} Model {c |} {res} 12877.2088 29 444.041682 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 2.55533515 124 .020607542 {txt}R-squared ={res} 0.9998 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9998 {txt} Total {c |} {res} 12879.7641 153 84.1814649 {txt}Root MSE = {res} .14355 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 9}age {c |}{col 14}{res}{space 2}-17.61686{col 26}{space 2} .0823013{col 37}{space 1} -214.05{col 46}{space 3}0.000{col 54}{space 4}-17.77975{col 67}{space 3}-17.45396 {txt}{space 8}size {c |}{col 14}{res}{space 2} 48.50865{col 26}{space 2} .3157276{col 37}{space 1} 153.64{col 46}{space 3}0.000{col 54}{space 4} 47.88374{col 67}{space 3} 49.13357 {txt}{space 7}size2 {c |}{col 14}{res}{space 2}-2.067242{col 26}{space 2} .014843{col 37}{space 1} -139.27{col 46}{space 3}0.000{col 54}{space 4}-2.096621{col 67}{space 3}-2.037864 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-4.056942{col 26}{space 2} .1054668{col 37}{space 1} -38.47{col 46}{space 3}0.000{col 54}{space 4} -4.26569{col 67}{space 3}-3.848194 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} 2.866126{col 26}{space 2} .1011856{col 37}{space 1} 28.33{col 46}{space 3}0.000{col 54}{space 4} 2.665851{col 67}{space 3} 3.0664 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .7191738{col 26}{space 2} .083416{col 37}{space 1} 8.62{col 46}{space 3}0.000{col 54}{space 4} .5540701{col 67}{space 3} .8842775 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-5.588221{col 26}{space 2} .0707197{col 37}{space 1} -79.02{col 46}{space 3}0.000{col 54}{space 4}-5.728195{col 67}{space 3}-5.448247 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-6.228404{col 26}{space 2} .0710025{col 37}{space 1} -87.72{col 46}{space 3}0.000{col 54}{space 4}-6.368938{col 67}{space 3}-6.087871 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-2.414936{col 26}{space 2} .0695745{col 37}{space 1} -34.71{col 46}{space 3}0.000{col 54}{space 4}-2.552644{col 67}{space 3}-2.277229 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-.7233602{col 26}{space 2} .0721141{col 37}{space 1} -10.03{col 46}{space 3}0.000{col 54}{space 4}-.8660941{col 67}{space 3}-.5806262 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} 3.863512{col 26}{space 2} .0662694{col 37}{space 1} 58.30{col 46}{space 3}0.000{col 54}{space 4} 3.732346{col 67}{space 3} 3.994678 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-8.262066{col 26}{space 2} .0572704{col 37}{space 1} -144.26{col 46}{space 3}0.000{col 54}{space 4} -8.37542{col 67}{space 3}-8.148712 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-9.723176{col 26}{space 2} .0555122{col 37}{space 1} -175.15{col 46}{space 3}0.000{col 54}{space 4} -9.83305{col 67}{space 3}-9.613302 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-13.06618{col 26}{space 2} .054976{col 37}{space 1} -237.67{col 46}{space 3}0.000{col 54}{space 4}-13.17499{col 67}{space 3}-12.95736 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-3.465193{col 26}{space 2} .0542679{col 37}{space 1} -63.85{col 46}{space 3}0.000{col 54}{space 4}-3.572604{col 67}{space 3}-3.357782 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-7.322781{col 26}{space 2} .0528949{col 37}{space 1} -138.44{col 46}{space 3}0.000{col 54}{space 4}-7.427474{col 67}{space 3}-7.218087 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} -3.23473{col 26}{space 2} .0537095{col 37}{space 1} -60.23{col 46}{space 3}0.000{col 54}{space 4}-3.341036{col 67}{space 3}-3.128424 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 41.2649{col 26}{space 2} .1299067{col 37}{space 1} 317.65{col 46}{space 3}0.000{col 54}{space 4} 41.00778{col 67}{space 3} 41.52202 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 43.62906{col 26}{space 2} .1418998{col 37}{space 1} 307.46{col 46}{space 3}0.000{col 54}{space 4} 43.3482{col 67}{space 3} 43.90992 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 27.63367{col 26}{space 2} .1228733{col 37}{space 1} 224.90{col 46}{space 3}0.000{col 54}{space 4} 27.39047{col 67}{space 3} 27.87687 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 45.84651{col 26}{space 2} .1320083{col 37}{space 1} 347.30{col 46}{space 3}0.000{col 54}{space 4} 45.58523{col 67}{space 3} 46.10779 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} 9.718269{col 26}{space 2} .0773838{col 37}{space 1} 125.59{col 46}{space 3}0.000{col 54}{space 4} 9.565105{col 67}{space 3} 9.871433 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 29.39947{col 26}{space 2} .1226288{col 37}{space 1} 239.74{col 46}{space 3}0.000{col 54}{space 4} 29.15675{col 67}{space 3} 29.64219 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 32.2527{col 26}{space 2} .1240747{col 37}{space 1} 259.95{col 46}{space 3}0.000{col 54}{space 4} 32.00713{col 67}{space 3} 32.49828 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 30.95654{col 26}{space 2} .1347321{col 37}{space 1} 229.76{col 46}{space 3}0.000{col 54}{space 4} 30.68987{col 67}{space 3} 31.22322 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} 10.58404{col 26}{space 2} .121176{col 37}{space 1} 87.34{col 46}{space 3}0.000{col 54}{space 4} 10.3442{col 67}{space 3} 10.82388 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-12.48145{col 26}{space 2} .0822586{col 37}{space 1} -151.73{col 46}{space 3}0.000{col 54}{space 4}-12.64426{col 67}{space 3}-12.31863 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-4.276118{col 26}{space 2} .0665854{col 37}{space 1} -64.22{col 46}{space 3}0.000{col 54}{space 4}-4.407909{col 67}{space 3}-4.144326 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 27.12259{col 26}{space 2} .1205331{col 37}{space 1} 225.02{col 46}{space 3}0.000{col 54}{space 4} 26.88402{col 67}{space 3} 27.36116 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-245.7641{col 26}{space 2} 1.753137{col 37}{space 1} -140.19{col 46}{space 3}0.000{col 54}{space 4} -249.234{col 67}{space 3}-242.2941 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 1127 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.474 {res}First component regression {txt}(sum of wgt is 8.9463e+01) Linear regression Number of obs = {res} 189 {txt}F(28, 160) = {res} 139.11 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.9064 {txt}Root MSE = {res} .02922 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0111184{col 26}{space 2} .0022389{col 37}{space 1} 4.97{col 46}{space 3}0.000{col 54}{space 4} .0066968{col 67}{space 3} .0155399 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .103557{col 26}{space 2} .016469{col 37}{space 1} 6.29{col 46}{space 3}0.000{col 54}{space 4} .0710324{col 67}{space 3} .1360816 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0478333{col 26}{space 2} .0250387{col 37}{space 1} 1.91{col 46}{space 3}0.058{col 54}{space 4}-.0016157{col 67}{space 3} .0972822 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0297402{col 26}{space 2} .0130877{col 37}{space 1} 2.27{col 46}{space 3}0.024{col 54}{space 4} .0038934{col 67}{space 3} .055587 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0117968{col 26}{space 2} .0138974{col 37}{space 1} 0.85{col 46}{space 3}0.397{col 54}{space 4}-.0156491{col 67}{space 3} .0392427 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0029199{col 26}{space 2} .0120854{col 37}{space 1} -0.24{col 46}{space 3}0.809{col 54}{space 4}-.0267874{col 67}{space 3} .0209476 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .0204448{col 26}{space 2} .014113{col 37}{space 1} 1.45{col 46}{space 3}0.149{col 54}{space 4} -.007427{col 67}{space 3} .0483166 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0162245{col 26}{space 2} .0131614{col 37}{space 1} -1.23{col 46}{space 3}0.219{col 54}{space 4}-.0422171{col 67}{space 3} .009768 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0104867{col 26}{space 2} .0151122{col 37}{space 1} 0.69{col 46}{space 3}0.489{col 54}{space 4}-.0193585{col 67}{space 3} .0403318 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0111851{col 26}{space 2} .0167643{col 37}{space 1} -0.67{col 46}{space 3}0.506{col 54}{space 4}-.0442929{col 67}{space 3} .0219227 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0454026{col 26}{space 2} .0110302{col 37}{space 1} 4.12{col 46}{space 3}0.000{col 54}{space 4} .023619{col 67}{space 3} .0671861 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .011493{col 26}{space 2} .0178845{col 37}{space 1} 0.64{col 46}{space 3}0.521{col 54}{space 4}-.0238272{col 67}{space 3} .0468132 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0939389{col 26}{space 2} .0146156{col 37}{space 1} 6.43{col 46}{space 3}0.000{col 54}{space 4} .0650746{col 67}{space 3} .1228032 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0089854{col 26}{space 2} .0152769{col 37}{space 1} 0.59{col 46}{space 3}0.557{col 54}{space 4}-.0211851{col 67}{space 3} .0391558 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0039978{col 26}{space 2} .017255{col 37}{space 1} 0.23{col 46}{space 3}0.817{col 54}{space 4}-.0300791{col 67}{space 3} .0380748 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0429939{col 26}{space 2} .0232119{col 37}{space 1} -1.85{col 46}{space 3}0.066{col 54}{space 4} -.088835{col 67}{space 3} .0028473 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.0579959{col 26}{space 2} .013576{col 37}{space 1} -4.27{col 46}{space 3}0.000{col 54}{space 4}-.0848071{col 67}{space 3}-.0311847 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} -.112402{col 26}{space 2} .0127168{col 37}{space 1} -8.84{col 46}{space 3}0.000{col 54}{space 4}-.1375165{col 67}{space 3}-.0872876 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.0741137{col 26}{space 2} .015545{col 37}{space 1} -4.77{col 46}{space 3}0.000{col 54}{space 4}-.1048136{col 67}{space 3}-.0434138 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1316642{col 26}{space 2} .0149651{col 37}{space 1} -8.80{col 46}{space 3}0.000{col 54}{space 4}-.1612187{col 67}{space 3}-.1021096 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1293087{col 26}{space 2} .0147476{col 37}{space 1} -8.77{col 46}{space 3}0.000{col 54}{space 4}-.1584338{col 67}{space 3}-.1001837 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .1694327{col 26}{space 2} .0389896{col 37}{space 1} 4.35{col 46}{space 3}0.000{col 54}{space 4} .0924322{col 67}{space 3} .2464332 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.0916815{col 26}{space 2} .0121842{col 37}{space 1} -7.52{col 46}{space 3}0.000{col 54}{space 4}-.1157441{col 67}{space 3}-.0676189 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1044812{col 26}{space 2} .016813{col 37}{space 1} -6.21{col 46}{space 3}0.000{col 54}{space 4}-.1376853{col 67}{space 3}-.0712772 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .0260821{col 26}{space 2} .0130314{col 37}{space 1} 2.00{col 46}{space 3}0.047{col 54}{space 4} .0003464{col 67}{space 3} .0518178 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1040227{col 26}{space 2} .0172953{col 37}{space 1} -6.01{col 46}{space 3}0.000{col 54}{space 4}-.1381792{col 67}{space 3}-.0698662 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0584866{col 26}{space 2} .0187178{col 37}{space 1} -3.12{col 46}{space 3}0.002{col 54}{space 4}-.0954523{col 67}{space 3}-.0215208 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.0730509{col 26}{space 2} .0101383{col 37}{space 1} -7.21{col 46}{space 3}0.000{col 54}{space 4} -.093073{col 67}{space 3}-.0530288 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .080708{col 26}{space 2} .0088646{col 37}{space 1} 9.10{col 46}{space 3}0.000{col 54}{space 4} .0632014{col 67}{space 3} .0982147 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 9.9537e+01) Linear regression Number of obs = {res} 148 {txt}{help j_robustsingular:F(26, 119) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.6560 {txt}Root MSE = {res} .06852 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2}-.0048226{col 26}{space 2} .0036789{col 37}{space 1} -1.31{col 46}{space 3}0.192{col 54}{space 4}-.0121071{col 67}{space 3} .002462 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0245843{col 26}{space 2} .0115714{col 37}{space 1} -2.12{col 46}{space 3}0.036{col 54}{space 4}-.0474968{col 67}{space 3}-.0016717 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} -.085476{col 26}{space 2} .0296203{col 37}{space 1} -2.89{col 46}{space 3}0.005{col 54}{space 4}-.1441273{col 67}{space 3}-.0268248 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0941375{col 26}{space 2} .0359079{col 37}{space 1} -2.62{col 46}{space 3}0.010{col 54}{space 4}-.1652388{col 67}{space 3}-.0230362 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .016982{col 26}{space 2} .0335381{col 37}{space 1} 0.51{col 46}{space 3}0.614{col 54}{space 4}-.0494269{col 67}{space 3} .0833908 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0331425{col 26}{space 2} .0494687{col 37}{space 1} -0.67{col 46}{space 3}0.504{col 54}{space 4}-.1310955{col 67}{space 3} .0648105 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0540295{col 26}{space 2} .0429824{col 37}{space 1} -1.26{col 46}{space 3}0.211{col 54}{space 4} -.139139{col 67}{space 3} .03108 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-.0142717{col 26}{space 2} .0426728{col 37}{space 1} -0.33{col 46}{space 3}0.739{col 54}{space 4} -.098768{col 67}{space 3} .0702247 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-.0825052{col 26}{space 2} .0311307{col 37}{space 1} -2.65{col 46}{space 3}0.009{col 54}{space 4} -.144147{col 67}{space 3}-.0208633 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0207178{col 26}{space 2} .0269703{col 37}{space 1} -0.77{col 46}{space 3}0.444{col 54}{space 4}-.0741217{col 67}{space 3} .0326861 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.0267631{col 26}{space 2} .03241{col 37}{space 1} -0.83{col 46}{space 3}0.411{col 54}{space 4}-.0909382{col 67}{space 3} .037412 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} -.019562{col 26}{space 2} .035332{col 37}{space 1} -0.55{col 46}{space 3}0.581{col 54}{space 4} -.089523{col 67}{space 3} .0503989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0637359{col 26}{space 2} .0329423{col 37}{space 1} -1.93{col 46}{space 3}0.055{col 54}{space 4}-.1289649{col 67}{space 3} .0014932 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0326186{col 26}{space 2} .0333059{col 37}{space 1} -0.98{col 46}{space 3}0.329{col 54}{space 4}-.0985676{col 67}{space 3} .0333303 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0450185{col 26}{space 2} .0328839{col 37}{space 1} -1.37{col 46}{space 3}0.174{col 54}{space 4}-.1101319{col 67}{space 3} .0200949 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0067319{col 26}{space 2} .0336108{col 37}{space 1} 0.20{col 46}{space 3}0.842{col 54}{space 4}-.0598208{col 67}{space 3} .0732847 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.2711394{col 26}{space 2} .0336174{col 37}{space 1} -8.07{col 46}{space 3}0.000{col 54}{space 4}-.3377051{col 67}{space 3}-.2045736 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1008434{col 26}{space 2} .0391087{col 37}{space 1} -2.58{col 46}{space 3}0.011{col 54}{space 4}-.1782826{col 67}{space 3}-.0234042 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.2026884{col 26}{space 2} .0343164{col 37}{space 1} -5.91{col 46}{space 3}0.000{col 54}{space 4}-.2706384{col 67}{space 3}-.1347385 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .0301905{col 26}{space 2} .0380979{col 37}{space 1} 0.79{col 46}{space 3}0.430{col 54}{space 4} -.045247{col 67}{space 3} .1056281 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1425483{col 26}{space 2} .0365557{col 37}{space 1} -3.90{col 46}{space 3}0.000{col 54}{space 4}-.2149322{col 67}{space 3}-.0701644 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.2383276{col 26}{space 2} .0377807{col 37}{space 1} -6.31{col 46}{space 3}0.000{col 54}{space 4}-.3131371{col 67}{space 3}-.1635181 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1983006{col 26}{space 2} .0300698{col 37}{space 1} -6.59{col 46}{space 3}0.000{col 54}{space 4}-.2578418{col 67}{space 3}-.1387595 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.0886402{col 26}{space 2} .0418654{col 37}{space 1} -2.12{col 46}{space 3}0.036{col 54}{space 4}-.1715379{col 67}{space 3}-.0057426 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} -.171757{col 26}{space 2} .0647102{col 37}{space 1} -2.65{col 46}{space 3}0.009{col 54}{space 4}-.2998896{col 67}{space 3}-.0436243 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1871218{col 26}{space 2} .0358947{col 37}{space 1} -5.21{col 46}{space 3}0.000{col 54}{space 4}-.2581969{col 67}{space 3}-.1160467 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.1569515{col 26}{space 2} .031959{col 37}{space 1} -4.91{col 46}{space 3}0.000{col 54}{space 4}-.2202337{col 67}{space 3}-.0936694 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.2587264{col 26}{space 2} .0338947{col 37}{space 1} -7.63{col 46}{space 3}0.000{col 54}{space 4}-.3258413{col 67}{space 3}-.1916114 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3958073{col 26}{space 2} .0386796{col 37}{space 1} 10.23{col 46}{space 3}0.000{col 54}{space 4} .3192179{col 67}{space 3} .4723968 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(215 real changes made) {res}This Switching Regression took {com}45{res} seconds. {txt}(194 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00174 Average of the probability vector is: {txt}0.492 On iteration {com}50{txt} greatest diff is: {com}0.021865 on yr4 in the{txt} first main {com}eqn {txt}Log-likelihood is : 330.79049 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00072 Average of the probability vector is: {txt}0.477 On iteration {com}100{txt} greatest diff is: {com}-0.111653 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 334.62145 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00033 Average of the probability vector is: {txt}0.469 On iteration {com}150{txt} greatest diff is: {com}0.004628 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 338.37111 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00036 Average of the probability vector is: {txt}0.464 On iteration {com}200{txt} greatest diff is: {com}0.004789 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 340.7751 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00026 Average of the probability vector is: {txt}0.461 On iteration {com}250{txt} greatest diff is: {com}0.003182 on yr4 in the{txt} first main {com}eqn {txt}Log-likelihood is : 342.81867 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00014 Average of the probability vector is: {txt}0.458 On iteration {com}300{txt} greatest diff is: {com}0.000734 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 344.46886 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.457 On iteration {com}350{txt} greatest diff is: {com}0.000538 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 345.56269 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.457 On iteration {com}400{txt} greatest diff is: {com}0.000328 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 346.39674 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.456 On iteration {com}450{txt} greatest diff is: {com}0.000264 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 347.05226 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.456 On iteration {com}500{txt} greatest diff is: {com}0.000216 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 347.58198 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.456 On iteration {com}550{txt} greatest diff is: {com}0.000163 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 348.0117 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.455 On iteration {com}600{txt} greatest diff is: {com}0.000136 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 348.36647 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.455 On iteration {com}650{txt} greatest diff is: {com}0.000112 on yr5 in the{txt} first main {com}eqn {txt}Log-likelihood is : 348.66861 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 8.9e-108 5.7e-241 {txt} 5% {res} 9.15e-44 8.2e-122 {txt}10% {res} 6.63e-31 8.9e-108 {txt}Obs {res} 207 {txt}25% {res} 7.48e-19 2.00e-86 {txt}Sum of Wgt. {res} 207 {txt}50% {res} .0722086 {txt}Mean {res} .4548868 {txt}Largest Std. Dev. {res} .484032 {txt}75% {res} 1 1 {txt}90% {res} 1 1 {txt}Variance {res} .234287 {txt}95% {res} 1 1 {txt}Skewness {res} .1866562 {txt}99% {res} 1 1 {txt}Kurtosis {res} 1.057619 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 172 {txt}{hline 13}{c +}{hline 34} F(31, 140) = {res} 30665.90 {txt} Model {c |} {res} 9142.31775 31 294.913476 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.34637788 140 .009616985 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9998 {txt} Total {c |} {res} 9143.66413 171 53.47172 {txt}Root MSE = {res} .09807 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 6}kz_cfs {c |}{col 14}{res}{space 2}-.8226365{col 26}{space 2} .0068956{col 37}{space 1} -119.30{col 46}{space 3}0.000{col 54}{space 4}-.8362696{col 67}{space 3}-.8090034 {txt}{space 9}mtb {c |}{col 14}{res}{space 2}-.6215873{col 26}{space 2} .0013681{col 37}{space 1} -454.33{col 46}{space 3}0.000{col 54}{space 4}-.6242922{col 67}{space 3}-.6188825 {txt}{space 8}kz_d {c |}{col 14}{res}{space 2}-14.23374{col 26}{space 2} .0755573{col 37}{space 1} -188.38{col 46}{space 3}0.000{col 54}{space 4}-14.38312{col 67}{space 3}-14.08436 {txt}{space 6}kz_div {c |}{col 14}{res}{space 2} 20.1936{col 26}{space 2} .0971238{col 37}{space 1} 207.92{col 46}{space 3}0.000{col 54}{space 4} 20.00158{col 67}{space 3} 20.38562 {txt}{space 7}kz_cs {c |}{col 14}{res}{space 2} -2.82752{col 26}{space 2} .0220681{col 37}{space 1} -128.13{col 46}{space 3}0.000{col 54}{space 4} -2.87115{col 67}{space 3} -2.78389 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-10.81004{col 26}{space 2} .0468222{col 37}{space 1} -230.87{col 46}{space 3}0.000{col 54}{space 4}-10.90261{col 67}{space 3}-10.71747 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-5.951359{col 26}{space 2} .0401879{col 37}{space 1} -148.09{col 46}{space 3}0.000{col 54}{space 4}-6.030812{col 67}{space 3}-5.871905 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -15.997{col 26}{space 2} .0416189{col 37}{space 1} -384.37{col 46}{space 3}0.000{col 54}{space 4}-16.07928{col 67}{space 3}-15.91472 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} 2.31502{col 26}{space 2} .0434479{col 37}{space 1} 53.28{col 46}{space 3}0.000{col 54}{space 4} 2.229121{col 67}{space 3} 2.400919 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -1.32677{col 26}{space 2} .0382508{col 37}{space 1} -34.69{col 46}{space 3}0.000{col 54}{space 4}-1.402394{col 67}{space 3}-1.251146 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} 8.572867{col 26}{space 2} .0449842{col 37}{space 1} 190.58{col 46}{space 3}0.000{col 54}{space 4} 8.483931{col 67}{space 3} 8.661803 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} 9.081809{col 26}{space 2} .0406568{col 37}{space 1} 223.38{col 46}{space 3}0.000{col 54}{space 4} 9.001428{col 67}{space 3} 9.162189 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-2.804311{col 26}{space 2} .0345242{col 37}{space 1} -81.23{col 46}{space 3}0.000{col 54}{space 4}-2.872567{col 67}{space 3}-2.736055 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-4.041837{col 26}{space 2} .0341061{col 37}{space 1} -118.51{col 46}{space 3}0.000{col 54}{space 4}-4.109267{col 67}{space 3}-3.974407 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-4.860753{col 26}{space 2} .0339518{col 37}{space 1} -143.17{col 46}{space 3}0.000{col 54}{space 4}-4.927877{col 67}{space 3}-4.793628 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-6.294771{col 26}{space 2} .0342733{col 37}{space 1} -183.66{col 46}{space 3}0.000{col 54}{space 4}-6.362531{col 67}{space 3}-6.227011 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} -2.2688{col 26}{space 2} .034436{col 37}{space 1} -65.88{col 46}{space 3}0.000{col 54}{space 4}-2.336882{col 67}{space 3}-2.200718 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} -3.62077{col 26}{space 2} .0339977{col 37}{space 1} -106.50{col 46}{space 3}0.000{col 54}{space 4}-3.687986{col 67}{space 3}-3.553555 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} 5.784721{col 26}{space 2} .0368756{col 37}{space 1} 156.87{col 46}{space 3}0.000{col 54}{space 4} 5.711816{col 67}{space 3} 5.857626 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 16.67164{col 26}{space 2} .0514786{col 37}{space 1} 323.86{col 46}{space 3}0.000{col 54}{space 4} 16.56986{col 67}{space 3} 16.77341 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 30.11057{col 26}{space 2} .0652911{col 37}{space 1} 461.17{col 46}{space 3}0.000{col 54}{space 4} 29.98149{col 67}{space 3} 30.23965 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 15.76773{col 26}{space 2} .049944{col 37}{space 1} 315.71{col 46}{space 3}0.000{col 54}{space 4} 15.66898{col 67}{space 3} 15.86647 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 19.95619{col 26}{space 2} .0527917{col 37}{space 1} 378.02{col 46}{space 3}0.000{col 54}{space 4} 19.85182{col 67}{space 3} 20.06056 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-3.522999{col 26}{space 2} .0364756{col 37}{space 1} -96.58{col 46}{space 3}0.000{col 54}{space 4}-3.595114{col 67}{space 3}-3.450885 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 3.988738{col 26}{space 2} .0378019{col 37}{space 1} 105.52{col 46}{space 3}0.000{col 54}{space 4} 3.914002{col 67}{space 3} 4.063474 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 30.54171{col 26}{space 2} .1139513{col 37}{space 1} 268.02{col 46}{space 3}0.000{col 54}{space 4} 30.31643{col 67}{space 3} 30.767 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 25.08698{col 26}{space 2} .07277{col 37}{space 1} 344.74{col 46}{space 3}0.000{col 54}{space 4} 24.94311{col 67}{space 3} 25.23085 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-7.143626{col 26}{space 2} .0565275{col 37}{space 1} -126.37{col 46}{space 3}0.000{col 54}{space 4}-7.255384{col 67}{space 3}-7.031868 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 5.519791{col 26}{space 2} .038311{col 37}{space 1} 144.08{col 46}{space 3}0.000{col 54}{space 4} 5.444049{col 67}{space 3} 5.595534 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-3.166566{col 26}{space 2} .0444365{col 37}{space 1} -71.26{col 46}{space 3}0.000{col 54}{space 4}-3.254419{col 67}{space 3}-3.078712 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-2.620848{col 26}{space 2} .0399615{col 37}{space 1} -65.58{col 46}{space 3}0.000{col 54}{space 4}-2.699854{col 67}{space 3}-2.541842 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1165367{col 26}{space 2} .0375241{col 37}{space 1} 3.11{col 46}{space 3}0.002{col 54}{space 4} .0423495{col 67}{space 3} .1907238 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 681 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.455 {res}First component regression {txt}(sum of wgt is 9.3954e+01) Linear regression Number of obs = {res} 207 {txt}F(28, 178) = {res} 180.70 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.9267 {txt}Root MSE = {res} .02128 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0135949{col 26}{space 2} .0009261{col 37}{space 1} 14.68{col 46}{space 3}0.000{col 54}{space 4} .0117674{col 67}{space 3} .0154225 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0555653{col 26}{space 2} .0050503{col 37}{space 1} 11.00{col 46}{space 3}0.000{col 54}{space 4} .0455992{col 67}{space 3} .0655315 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0687882{col 26}{space 2} .0196116{col 37}{space 1} 3.51{col 46}{space 3}0.001{col 54}{space 4} .030087{col 67}{space 3} .1074894 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0371024{col 26}{space 2} .008934{col 37}{space 1} 4.15{col 46}{space 3}0.000{col 54}{space 4} .0194722{col 67}{space 3} .0547325 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0138659{col 26}{space 2} .0116873{col 37}{space 1} 1.19{col 46}{space 3}0.237{col 54}{space 4}-.0091977{col 67}{space 3} .0369294 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .0102013{col 26}{space 2} .0088306{col 37}{space 1} 1.16{col 46}{space 3}0.250{col 54}{space 4}-.0072247{col 67}{space 3} .0276274 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.005434{col 26}{space 2} .0136632{col 37}{space 1} -0.40{col 46}{space 3}0.691{col 54}{space 4}-.0323968{col 67}{space 3} .0215287 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0125575{col 26}{space 2} .0111106{col 37}{space 1} 1.13{col 46}{space 3}0.260{col 54}{space 4}-.0093678{col 67}{space 3} .0344829 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0191998{col 26}{space 2} .0076136{col 37}{space 1} 2.52{col 46}{space 3}0.013{col 54}{space 4} .0041752{col 67}{space 3} .0342243 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0543701{col 26}{space 2} .0091172{col 37}{space 1} 5.96{col 46}{space 3}0.000{col 54}{space 4} .0363785{col 67}{space 3} .0723617 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0945267{col 26}{space 2} .0183283{col 37}{space 1} 5.16{col 46}{space 3}0.000{col 54}{space 4} 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.0141142{col 37}{space 1} -16.59{col 46}{space 3}0.000{col 54}{space 4}-.2619766{col 67}{space 3}-.2062711 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2973524{col 26}{space 2} .0124813{col 37}{space 1} -23.82{col 46}{space 3}0.000{col 54}{space 4}-.3219827{col 67}{space 3} -.272722 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.2502743{col 26}{space 2} .0136824{col 37}{space 1} -18.29{col 46}{space 3}0.000{col 54}{space 4}-.2772749{col 67}{space 3}-.2232737 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.2907954{col 26}{space 2} .0140379{col 37}{space 1} -20.72{col 46}{space 3}0.000{col 54}{space 4}-.3184975{col 67}{space 3}-.2630933 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1297226{col 26}{space 2} .0112856{col 37}{space 1} -11.49{col 46}{space 3}0.000{col 54}{space 4}-.1519934{col 67}{space 3}-.1074518 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.2637838{col 26}{space 2} .0136592{col 37}{space 1} -19.31{col 46}{space 3}0.000{col 54}{space 4}-.2907386{col 67}{space 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1} -23.21{col 46}{space 3}0.000{col 54}{space 4}-.2958642{col 67}{space 3}-.2494973 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2524754{col 26}{space 2} .0119228{col 37}{space 1} 21.18{col 46}{space 3}0.000{col 54}{space 4} .2289473{col 67}{space 3} .2760036 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 1.1305e+02) Linear regression Number of obs = {res} 171 {txt}{help j_robustsingular:F(27, 142) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.4969 {txt}Root MSE = {res} .09486 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0056359{col 26}{space 2} .0061399{col 37}{space 1} 0.92{col 46}{space 3}0.360{col 54}{space 4}-.0065015{col 67}{space 3} .0177733 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0141821{col 26}{space 2} .0148857{col 37}{space 1} -0.95{col 46}{space 3}0.342{col 54}{space 4}-.0436084{col 67}{space 3} .0152441 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.1395463{col 26}{space 2} .0391899{col 37}{space 1} -3.56{col 46}{space 3}0.001{col 54}{space 4}-.2170174{col 67}{space 3}-.0620752 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.1203172{col 26}{space 2} .0447477{col 37}{space 1} -2.69{col 46}{space 3}0.008{col 54}{space 4} -.208775{col 67}{space 3}-.0318594 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0680268{col 26}{space 2} .0400696{col 37}{space 1} -1.70{col 46}{space 3}0.092{col 54}{space 4}-.1472369{col 67}{space 3} .0111832 {txt}{space 9}yr5 {c |}{col 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{txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1539107{col 26}{space 2} .0417328{col 37}{space 1} -3.69{col 46}{space 3}0.000{col 54}{space 4}-.2364086{col 67}{space 3}-.0714128 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.0267236{col 26}{space 2} .0621858{col 37}{space 1} -0.43{col 46}{space 3}0.668{col 54}{space 4}-.1496531{col 67}{space 3} .096206 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0612847{col 26}{space 2} .0526155{col 37}{space 1} -1.16{col 46}{space 3}0.246{col 54}{space 4}-.1652956{col 67}{space 3} .0427263 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0698603{col 26}{space 2} .0452437{col 37}{space 1} -1.54{col 46}{space 3}0.125{col 54}{space 4}-.1592985{col 67}{space 3} .0195779 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -.00674{col 26}{space 2} .0460426{col 37}{space 1} -0.15{col 46}{space 3}0.884{col 54}{space 4}-.0977576{col 67}{space 3} .0842776 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1464865{col 26}{space 2} .0425619{col 37}{space 1} -3.44{col 46}{space 3}0.001{col 54}{space 4}-.2306234{col 67}{space 3}-.0623496 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2402215{col 26}{space 2} .0506909{col 37}{space 1} 4.74{col 46}{space 3}0.000{col 54}{space 4} .1400151{col 67}{space 3} .3404279 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(221 real changes made) {res}This Switching Regression took {com}37{res} seconds. {txt}(205 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00097 Average of the probability vector is: {txt}0.505 On iteration {com}50{txt} greatest diff is: {com}-2.1e+00 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 321.49026 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00081 Average of the probability vector is: {txt}0.496 On iteration {com}100{txt} greatest diff is: {com}0.021852 on yr9 in the{txt} second main {com}eqn {txt}Log-likelihood is : 322.64318 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00084 Average of the probability vector is: {txt}0.490 On iteration {com}150{txt} greatest diff is: {com}-0.114524 on f7 in the{txt} second main {com}eqn {txt}Log-likelihood is : 323.90845 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00050 Average of the probability vector is: {txt}0.476 On iteration {com}200{txt} greatest diff is: {com}-0.065052 on yr12 in the{txt} second main {com}eqn {txt}Log-likelihood is : 326.64607 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00052 Average of the probability vector is: {txt}0.466 On iteration {com}250{txt} greatest diff is: {com}0.023464 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 328.02584 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00125 Average of the probability vector is: {txt}0.459 On iteration {com}300{txt} greatest diff is: {com}0.209030 on yr4 in the{txt} first main {com}eqn {txt}Log-likelihood is : 330.92163 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00027 Average of the probability vector is: {txt}0.456 On iteration {com}350{txt} greatest diff is: {com}-0.007185 on yr14 in the{txt} first main {com}eqn {txt}Log-likelihood is : 332.88676 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00018 Average of the probability vector is: {txt}0.453 On iteration {com}400{txt} greatest diff is: {com}-0.007305 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 334.08236 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00017 Average of the probability vector is: {txt}0.451 On iteration {com}450{txt} greatest diff is: {com}-0.035948 on yr12 in the{txt} first main {com}eqn {txt}Log-likelihood is : 335.06013 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00014 Average of the probability vector is: {txt}0.449 On iteration {com}500{txt} greatest diff is: {com}0.010632 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 335.99328 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.448 On iteration {com}550{txt} greatest diff is: {com}0.002477 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 336.77826 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.447 On iteration {com}600{txt} greatest diff is: {com}0.001213 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 337.42515 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.447 On iteration {com}650{txt} greatest diff is: {com}0.000756 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 337.97665 {res}This iteration took {com}0{res} second. On iter 700 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.447 On iteration {com}700{txt} greatest diff is: {com}0.000516 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 338.45827 {res}This iteration took {com}0{res} second. On iter 750 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.447 On iteration {com}750{txt} greatest diff is: {com}0.000401 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 338.88752 {res}This iteration took {com}0{res} second. On iter 800 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.447 On iteration {com}800{txt} greatest diff is: {com}0.000298 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 339.26955 {res}This iteration took {com}0{res} second. On iter 850 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.448 On iteration {com}850{txt} greatest diff is: {com}0.000247 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 339.61265 {res}This iteration took {com}0{res} second. On iter 900 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.448 On iteration {com}900{txt} greatest diff is: {com}0.000152 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 339.92337 {res}This iteration took {com}0{res} second. On iter 950 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.448 On iteration {com}950{txt} greatest diff is: {com}0.000161 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 340.20251 {res}This iteration took {com}0{res} second. On iter 1000 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.448 On iteration {com}1000{txt} greatest diff is: {com}0.000138 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 340.45517 {res}This iteration took {com}0{res} second. On iter 1050 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.448 On iteration {com}1050{txt} greatest diff is: {com}0.000116 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 340.68472 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 5.1e-135 2.3e-237 {txt} 5% {res} 8.72e-80 2.5e-155 {txt}10% {res} 2.33e-60 5.1e-135 {txt}Obs {res} 203 {txt}25% {res} 1.05e-21 3.9e-129 {txt}Sum of Wgt. {res} 203 {txt}50% {res} .2252531 {txt}Mean {res} .4476828 {txt}Largest Std. Dev. {res} .4674505 {txt}75% {res} .999983 1 {txt}90% {res} 1 1 {txt}Variance {res} .21851 {txt}95% {res} 1 1 {txt}Skewness {res} .2061637 {txt}99% {res} 1 1 {txt}Kurtosis {res} 1.133125 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 172 {txt}{hline 13}{c +}{hline 34} F(32, 139) = {res} 9820.22 {txt} Model {c |} {res} 12533.7516 32 391.679737 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 5.54401885 139 .039885028 {txt}R-squared ={res} 0.9996 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9995 {txt} Total {c |} {res} 12539.2956 171 73.329214 {txt}Root MSE = {res} .19971 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} fc{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 8}ww_d {c |}{col 14}{res}{space 2}-25.85345{col 26}{space 2} .1265528{col 37}{space 1} -204.29{col 46}{space 3}0.000{col 54}{space 4}-26.10367{col 67}{space 3}-25.60323 {txt}{space 6}ww_div {c |}{col 14}{res}{space 2} 8.810272{col 26}{space 2} .0585794{col 37}{space 1} 150.40{col 46}{space 3}0.000{col 54}{space 4} 8.69445{col 67}{space 3} 8.926094 {txt}{space 3}ww_gsales {c |}{col 14}{res}{space 2}-13.52274{col 26}{space 2} .1061651{col 37}{space 1} -127.37{col 46}{space 3}0.000{col 54}{space 4}-13.73264{col 67}{space 3}-13.31283 {txt}{space 5}ww_size {c |}{col 14}{res}{space 2} 1.187212{col 26}{space 2} .044649{col 37}{space 1} 26.59{col 46}{space 3}0.000{col 54}{space 4} 1.098933{col 67}{space 3} 1.275491 {txt}{space 7}ww_cs {c |}{col 14}{res}{space 2} 4.684433{col 26}{space 2} .1664871{col 37}{space 1} 28.14{col 46}{space 3}0.000{col 54}{space 4} 4.355258{col 67}{space 3} 5.013607 {txt}{space 6}ww_cfs {c |}{col 14}{res}{space 2} -3.8097{col 26}{space 2} .1370223{col 37}{space 1} -27.80{col 46}{space 3}0.000{col 54}{space 4}-4.080617{col 67}{space 3}-3.538783 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 1.407549{col 26}{space 2} .0861683{col 37}{space 1} 16.33{col 46}{space 3}0.000{col 54}{space 4} 1.237179{col 67}{space 3} 1.577919 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} 12.82479{col 26}{space 2} .1103134{col 37}{space 1} 116.26{col 46}{space 3}0.000{col 54}{space 4} 12.60668{col 67}{space 3} 13.0429 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .2521481{col 26}{space 2} .070268{col 37}{space 1} 3.59{col 46}{space 3}0.000{col 54}{space 4} .1132158{col 67}{space 3} .3910805 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} 10.77235{col 26}{space 2} .0924414{col 37}{space 1} 116.53{col 46}{space 3}0.000{col 54}{space 4} 10.58958{col 67}{space 3} 10.95513 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-1.256538{col 26}{space 2} .0760345{col 37}{space 1} -16.53{col 46}{space 3}0.000{col 54}{space 4}-1.406871{col 67}{space 3}-1.106204 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} 8.481327{col 26}{space 2} .0766677{col 37}{space 1} 110.62{col 46}{space 3}0.000{col 54}{space 4} 8.329742{col 67}{space 3} 8.632913 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} 17.6275{col 26}{space 2} .1039245{col 37}{space 1} 169.62{col 46}{space 3}0.000{col 54}{space 4} 17.42203{col 67}{space 3} 17.83298 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} 6.821656{col 26}{space 2} .0748987{col 37}{space 1} 91.08{col 46}{space 3}0.000{col 54}{space 4} 6.673568{col 67}{space 3} 6.969744 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} 9.63245{col 26}{space 2} .0830129{col 37}{space 1} 116.04{col 46}{space 3}0.000{col 54}{space 4} 9.468319{col 67}{space 3} 9.796581 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .6029246{col 26}{space 2} .0699855{col 37}{space 1} 8.61{col 46}{space 3}0.000{col 54}{space 4} .4645509{col 67}{space 3} .7412983 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-5.544718{col 26}{space 2} .067847{col 37}{space 1} -81.72{col 46}{space 3}0.000{col 54}{space 4}-5.678863{col 67}{space 3}-5.410572 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} 5.015773{col 26}{space 2} .0710526{col 37}{space 1} 70.59{col 46}{space 3}0.000{col 54}{space 4} 4.87529{col 67}{space 3} 5.156257 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.1822305{col 26}{space 2} .0710261{col 37}{space 1} -2.57{col 46}{space 3}0.011{col 54}{space 4}-.3226618{col 67}{space 3}-.0417992 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} 3.346922{col 26}{space 2} .0711954{col 37}{space 1} 47.01{col 46}{space 3}0.000{col 54}{space 4} 3.206156{col 67}{space 3} 3.487688 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 3.493317{col 26}{space 2} .1087317{col 37}{space 1} 32.13{col 46}{space 3}0.000{col 54}{space 4} 3.278335{col 67}{space 3} 3.708299 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 3.35492{col 26}{space 2} .1078343{col 37}{space 1} 31.11{col 46}{space 3}0.000{col 54}{space 4} 3.141713{col 67}{space 3} 3.568128 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 4.396588{col 26}{space 2} .0942958{col 37}{space 1} 46.63{col 46}{space 3}0.000{col 54}{space 4} 4.210149{col 67}{space 3} 4.583028 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 5.778916{col 26}{space 2} .1136026{col 37}{space 1} 50.87{col 46}{space 3}0.000{col 54}{space 4} 5.554304{col 67}{space 3} 6.003529 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-7.907634{col 26}{space 2} .0983414{col 37}{space 1} -80.41{col 46}{space 3}0.000{col 54}{space 4}-8.102073{col 67}{space 3}-7.713196 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 5.238734{col 26}{space 2} .0914364{col 37}{space 1} 57.29{col 46}{space 3}0.000{col 54}{space 4} 5.057948{col 67}{space 3} 5.41952 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 27.40091{col 26}{space 2} .1221044{col 37}{space 1} 224.41{col 46}{space 3}0.000{col 54}{space 4} 27.15948{col 67}{space 3} 27.64233 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .7081167{col 26}{space 2} .1088522{col 37}{space 1} 6.51{col 46}{space 3}0.000{col 54}{space 4} .4928965{col 67}{space 3} .9233369 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-6.251652{col 26}{space 2} .1376078{col 37}{space 1} -45.43{col 46}{space 3}0.000{col 54}{space 4}-6.523727{col 67}{space 3}-5.979577 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} -8.22156{col 26}{space 2} .087838{col 37}{space 1} -93.60{col 46}{space 3}0.000{col 54}{space 4}-8.395232{col 67}{space 3}-8.047889 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -1.88357{col 26}{space 2} .0923299{col 37}{space 1} -20.40{col 46}{space 3}0.000{col 54}{space 4}-2.066123{col 67}{space 3}-1.701018 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .0934981{col 26}{space 2} .0888208{col 37}{space 1} 1.05{col 46}{space 3}0.294{col 54}{space 4}-.0821165{col 67}{space 3} .2691126 {txt}{space 7}_cons {c |}{col 14}{res}{space 2}-24.27448{col 26}{space 2} .5075266{col 37}{space 1} -47.83{col 46}{space 3}0.000{col 54}{space 4}-25.27795{col 67}{space 3}-23.27101 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 1098 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.448 {res}First component regression {txt}(sum of wgt is 9.0578e+01) Linear regression Number of obs = {res} 203 {txt}{help j_robustsingular:F(26, 174) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.9341 {txt}Root MSE = {res} .02037 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .014857{col 26}{space 2} .0010596{col 37}{space 1} 14.02{col 46}{space 3}0.000{col 54}{space 4} .0127656{col 67}{space 3} .0169484 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0285019{col 26}{space 2} .0116076{col 37}{space 1} 2.46{col 46}{space 3}0.015{col 54}{space 4} .0055922{col 67}{space 3} .0514116 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .0197037{col 26}{space 2} .0173953{col 37}{space 1} 1.13{col 46}{space 3}0.259{col 54}{space 4}-.0146293{col 67}{space 3} .0540367 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0237164{col 26}{space 2} .0090009{col 37}{space 1} 2.63{col 46}{space 3}0.009{col 54}{space 4} .0059514{col 67}{space 3} .0414813 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0026751{col 26}{space 2} .0077752{col 37}{space 1} 0.34{col 46}{space 3}0.731{col 54}{space 4}-.0126708{col 67}{space 3} .018021 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .0105105{col 26}{space 2} .0091569{col 37}{space 1} 1.15{col 46}{space 3}0.253{col 54}{space 4}-.0075625{col 67}{space 3} .0285835 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .0143697{col 26}{space 2} .0069742{col 37}{space 1} 2.06{col 46}{space 3}0.041{col 54}{space 4} .0006049{col 67}{space 3} .0281345 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0079077{col 26}{space 2} .0098193{col 37}{space 1} 0.81{col 46}{space 3}0.422{col 54}{space 4}-.0114726{col 67}{space 3} .0272879 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .013574{col 26}{space 2} .0093228{col 37}{space 1} 1.46{col 46}{space 3}0.147{col 54}{space 4}-.0048264{col 67}{space 3} .0319743 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0176748{col 26}{space 2} .0120605{col 37}{space 1} 1.47{col 46}{space 3}0.145{col 54}{space 4}-.0061289{col 67}{space 3} .0414784 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .065825{col 26}{space 2} .0115094{col 37}{space 1} 5.72{col 46}{space 3}0.000{col 54}{space 4} .043109{col 67}{space 3} .0885409 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0892474{col 26}{space 2} .0084292{col 37}{space 1} 10.59{col 46}{space 3}0.000{col 54}{space 4} .0726107{col 67}{space 3} .105884 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0032451{col 26}{space 2} .008762{col 37}{space 1} -0.37{col 46}{space 3}0.712{col 54}{space 4}-.0205386{col 67}{space 3} .0140484 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0064667{col 26}{space 2} .0118831{col 37}{space 1} -0.54{col 46}{space 3}0.587{col 54}{space 4}-.0299202{col 67}{space 3} .0169868 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0048186{col 26}{space 2} .0098254{col 37}{space 1} 0.49{col 46}{space 3}0.624{col 54}{space 4}-.0145737{col 67}{space 3} .0242109 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0144521{col 26}{space 2} .010666{col 37}{space 1} -1.35{col 46}{space 3}0.177{col 54}{space 4}-.0355035{col 67}{space 3} .0065994 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.2216938{col 26}{space 2} .0135659{col 37}{space 1} -16.34{col 46}{space 3}0.000{col 54}{space 4}-.2484688{col 67}{space 3}-.1949188 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2821216{col 26}{space 2} .0136869{col 37}{space 1} -20.61{col 46}{space 3}0.000{col 54}{space 4}-.3091354{col 67}{space 3}-.2551079 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.2265833{col 26}{space 2} .0140031{col 37}{space 1} -16.18{col 46}{space 3}0.000{col 54}{space 4} -.254221{col 67}{space 3}-.1989456 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.3023272{col 26}{space 2} .0154858{col 37}{space 1} -19.52{col 46}{space 3}0.000{col 54}{space 4}-.3328914{col 67}{space 3}-.2717631 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1248382{col 26}{space 2} .0124083{col 37}{space 1} -10.06{col 46}{space 3}0.000{col 54}{space 4}-.1493284{col 67}{space 3} -.100348 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.2410029{col 26}{space 2} .01488{col 37}{space 1} -16.20{col 46}{space 3}0.000{col 54}{space 4}-.2703713{col 67}{space 3}-.2116344 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2613769{col 26}{space 2} .0147578{col 37}{space 1} -17.71{col 46}{space 3}0.000{col 54}{space 4}-.2905043{col 67}{space 3}-.2322495 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.2423802{col 26}{space 2} .0160253{col 37}{space 1} -15.12{col 46}{space 3}0.000{col 54}{space 4}-.2740091{col 67}{space 3}-.2107513 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.1589906{col 26}{space 2} .0163366{col 37}{space 1} -9.73{col 46}{space 3}0.000{col 54}{space 4}-.1912339{col 67}{space 3}-.1267472 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.2563755{col 26}{space 2} .0176652{col 37}{space 1} -14.51{col 46}{space 3}0.000{col 54}{space 4}-.2912412{col 67}{space 3}-.2215097 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.1630276{col 26}{space 2} .0144754{col 37}{space 1} -11.26{col 46}{space 3}0.000{col 54}{space 4}-.1915976{col 67}{space 3}-.1344575 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.2400101{col 26}{space 2} .0195903{col 37}{space 1} -12.25{col 46}{space 3}0.000{col 54}{space 4}-.2786753{col 67}{space 3}-.2013448 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .260564{col 26}{space 2} .0134089{col 37}{space 1} 19.43{col 46}{space 3}0.000{col 54}{space 4} .2340988{col 67}{space 3} .2870291 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 1.1242e+02) Linear regression Number of obs = {res} 170 {txt}F(28, 141) = {res} 168.52 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.4900 {txt}Root MSE = {res} .09382 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2}-.0009067{col 26}{space 2} .0048158{col 37}{space 1} -0.19{col 46}{space 3}0.851{col 54}{space 4}-.0104271{col 67}{space 3} .0086138 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0143263{col 26}{space 2} .0134187{col 37}{space 1} -1.07{col 46}{space 3}0.288{col 54}{space 4}-.0408541{col 67}{space 3} .0122015 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.1230053{col 26}{space 2} .0337364{col 37}{space 1} -3.65{col 46}{space 3}0.000{col 54}{space 4} -.1897{col 67}{space 3}-.0563107 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.1693853{col 26}{space 2} .0366944{col 37}{space 1} -4.62{col 46}{space 3}0.000{col 54}{space 4}-.2419276{col 67}{space 3} -.096843 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0795747{col 26}{space 2} .0404058{col 37}{space 1} -1.97{col 46}{space 3}0.051{col 54}{space 4}-.1594541{col 67}{space 3} .0003048 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .1330287{col 26}{space 2} .0531373{col 37}{space 1} 2.50{col 46}{space 3}0.013{col 54}{space 4} .0279799{col 67}{space 3} .2380776 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0201454{col 26}{space 2} .0404304{col 37}{space 1} -0.50{col 46}{space 3}0.619{col 54}{space 4}-.1000735{col 67}{space 3} .0597827 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .032778{col 26}{space 2} .068645{col 37}{space 1} 0.48{col 46}{space 3}0.634{col 54}{space 4}-.1029284{col 67}{space 3} .1684844 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .3151548{col 26}{space 2} .0475112{col 37}{space 1} 6.63{col 46}{space 3}0.000{col 54}{space 4} .2212285{col 67}{space 3} .4090811 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0229003{col 26}{space 2} .0561895{col 37}{space 1} 0.41{col 46}{space 3}0.684{col 54}{space 4}-.0881825{col 67}{space 3} .133983 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.0156216{col 26}{space 2} .0402809{col 37}{space 1} -0.39{col 46}{space 3}0.699{col 54}{space 4}-.0952543{col 67}{space 3} .064011 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0068543{col 26}{space 2} .0365191{col 37}{space 1} -0.19{col 46}{space 3}0.851{col 54}{space 4} -.07905{col 67}{space 3} .0653414 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0067681{col 26}{space 2} .0402146{col 37}{space 1} -0.17{col 46}{space 3}0.867{col 54}{space 4}-.0862697{col 67}{space 3} .0727335 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0023787{col 26}{space 2} .0475945{col 37}{space 1} 0.05{col 46}{space 3}0.960{col 54}{space 4}-.0917123{col 67}{space 3} .0964697 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0075329{col 26}{space 2} .0385781{col 37}{space 1} -0.20{col 46}{space 3}0.845{col 54}{space 4}-.0837991{col 67}{space 3} .0687334 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0687674{col 26}{space 2} .0431468{col 37}{space 1} 1.59{col 46}{space 3}0.113{col 54}{space 4}-.0165308{col 67}{space 3} .1540656 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} -.159481{col 26}{space 2} .0531019{col 37}{space 1} -3.00{col 46}{space 3}0.003{col 54}{space 4}-.2644599{col 67}{space 3}-.0545022 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.0700044{col 26}{space 2} .0472938{col 37}{space 1} -1.48{col 46}{space 3}0.141{col 54}{space 4}-.1635011{col 67}{space 3} .0234922 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1550261{col 26}{space 2} .0522951{col 37}{space 1} -2.96{col 46}{space 3}0.004{col 54}{space 4}-.2584099{col 67}{space 3}-.0516423 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} -.092315{col 26}{space 2} .0604123{col 37}{space 1} -1.53{col 46}{space 3}0.129{col 54}{space 4} -.211746{col 67}{space 3} .0271159 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0845289{col 26}{space 2} .0484902{col 37}{space 1} -1.74{col 46}{space 3}0.083{col 54}{space 4}-.1803907{col 67}{space 3} .0113328 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.0705109{col 26}{space 2} .0568651{col 37}{space 1} -1.24{col 46}{space 3}0.217{col 54}{space 4}-.1829293{col 67}{space 3} .0419076 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2151467{col 26}{space 2} .0461336{col 37}{space 1} -4.66{col 46}{space 3}0.000{col 54}{space 4}-.3063496{col 67}{space 3}-.1239438 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.0691203{col 26}{space 2} .0564209{col 37}{space 1} -1.23{col 46}{space 3}0.223{col 54}{space 4}-.1806606{col 67}{space 3} .04242 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0835608{col 26}{space 2} .0566712{col 37}{space 1} -1.47{col 46}{space 3}0.143{col 54}{space 4} -.195596{col 67}{space 3} .0284744 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1455431{col 26}{space 2} .0512104{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.2467826{col 67}{space 3}-.0443037 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0501352{col 26}{space 2} .0508228{col 37}{space 1} -0.99{col 46}{space 3}0.326{col 54}{space 4}-.1506084{col 67}{space 3} .0503379 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.2008809{col 26}{space 2} .0478641{col 37}{space 1} -4.20{col 46}{space 3}0.000{col 54}{space 4} -.295505{col 67}{space 3}-.1062569 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2801831{col 26}{space 2} .0564845{col 37}{space 1} 4.96{col 46}{space 3}0.000{col 54}{space 4} .1685171{col 67}{space 3} .3918492 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(206 real changes made) {res}This Switching Regression took {com}45{res} seconds. {txt}(208 real changes made) {com}. local regime hpd kzd wwd {txt} {com}. foreach l of local regime {c -(} {txt} 2{com}. switchr inv `l' {txt} 3{com}. gen byte inv`l'=fc>0.5 //storing classification results in binary {txt} 4{com}. replace fc=diba //reseting the initial values {txt} 5{com}. {c )-} {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00124 Average of the probability vector is: {txt}0.524 On iteration {com}50{txt} greatest diff is: {com}-0.008381 on yr14 in the{txt} second main {com}eqn {txt}Log-likelihood is : 297.00471 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00346 Average of the probability vector is: {txt}0.514 On iteration {com}100{txt} greatest diff is: {com}0.296872 on f9 in the{txt} first main {com}eqn {txt}Log-likelihood is : 299.92147 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00062 Average of the probability vector is: {txt}0.501 On iteration {com}150{txt} greatest diff is: {com}0.011132 on f9 in the{txt} first main {com}eqn {txt}Log-likelihood is : 304.71829 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00031 Average of the probability vector is: {txt}0.494 On iteration {com}200{txt} greatest diff is: {com}-0.018583 on yr10 in the{txt} first main {com}eqn {txt}Log-likelihood is : 306.44155 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00016 Average of the probability vector is: {txt}0.503 On iteration {com}250{txt} greatest diff is: {com}-0.005303 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 309.02511 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00010 Average of the probability vector is: {txt}0.503 On iteration {com}300{txt} greatest diff is: {com}-0.004124 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 308.8848 {res}This iteration took {com}0{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00007 Average of the probability vector is: {txt}0.503 On iteration {com}350{txt} greatest diff is: {com}-0.002588 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 308.57674 {res}This iteration took {com}0{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.503 On iteration {com}400{txt} greatest diff is: {com}-0.001651 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 308.19538 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.503 On iteration {com}450{txt} greatest diff is: {com}-0.001187 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 307.82995 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.503 On iteration {com}500{txt} greatest diff is: {com}-0.000837 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 307.50155 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.503 On iteration {com}550{txt} greatest diff is: {com}-0.000532 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 307.16346 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.503 On iteration {com}600{txt} greatest diff is: {com}-0.000250 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 306.89798 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.503 On iteration {com}650{txt} greatest diff is: {com}-0.000117 on yr6 in the{txt} second main {com}eqn {txt}Log-likelihood is : 306.67228 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 1.04e-51 7.84e-56 {txt} 5% {res} 9.22e-18 1.46e-55 {txt}10% {res} 1.42e-13 2.08e-51 {txt}Obs {res} 200 {txt}25% {res} .0000349 6.74e-48 {txt}Sum of Wgt. {res} 200 {txt}50% {res} .6015732 {txt}Mean {res} .5034787 {txt}Largest Std. Dev. {res} .4600395 {txt}75% {res} .9999996 1 {txt}90% {res} 1 1 {txt}Variance {res} .2116364 {txt}95% {res} 1 1 {txt}Skewness {res}-.0326403 {txt}99% {res} 1 1 {txt}Kurtosis {res} 1.126368 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 188 {txt}{hline 13}{c +}{hline 34} F(29, 158) = {res} 1656.37 {txt} Model {c |} {res} 5336.08264 29 184.00285 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 17.5519463 158 .111088268 {txt}R-squared ={res} 0.9967 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9961 {txt} Total {c |} {res} 5353.63459 187 28.629062 {txt}Root MSE = {res} .3333 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 9}age {c |}{col 14}{res}{space 2}-5.210229{col 26}{space 2} .1294411{col 37}{space 1} -40.25{col 46}{space 3}0.000{col 54}{space 4}-5.465887{col 67}{space 3} -4.95457 {txt}{space 8}size {c |}{col 14}{res}{space 2}-6.237217{col 26}{space 2} .2383172{col 37}{space 1} -26.17{col 46}{space 3}0.000{col 54}{space 4}-6.707915{col 67}{space 3}-5.766519 {txt}{space 7}size2 {c |}{col 14}{res}{space 2} .1233174{col 26}{space 2} .009075{col 37}{space 1} 13.59{col 46}{space 3}0.000{col 54}{space 4} .1053935{col 67}{space 3} .1412413 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 1.947747{col 26}{space 2} .1784241{col 37}{space 1} 10.92{col 46}{space 3}0.000{col 54}{space 4} 1.595343{col 67}{space 3} 2.300151 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.9548874{col 26}{space 2} .1455388{col 37}{space 1} -6.56{col 46}{space 3}0.000{col 54}{space 4} -1.24234{col 67}{space 3}-.6674349 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-2.563842{col 26}{space 2} .1449997{col 37}{space 1} -17.68{col 46}{space 3}0.000{col 54}{space 4} -2.85023{col 67}{space 3}-2.277455 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-2.424724{col 26}{space 2} .1458223{col 37}{space 1} -16.63{col 46}{space 3}0.000{col 54}{space 4}-2.712737{col 67}{space 3}-2.136712 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} 7.739212{col 26}{space 2} .1658362{col 37}{space 1} 46.67{col 46}{space 3}0.000{col 54}{space 4} 7.41167{col 67}{space 3} 8.066754 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} 5.778677{col 26}{space 2} .1376927{col 37}{space 1} 41.97{col 46}{space 3}0.000{col 54}{space 4} 5.506722{col 67}{space 3} 6.050633 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} 6.165113{col 26}{space 2} .1335162{col 37}{space 1} 46.18{col 46}{space 3}0.000{col 54}{space 4} 5.901407{col 67}{space 3} 6.42882 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} 1.394378{col 26}{space 2} .1214929{col 37}{space 1} 11.48{col 46}{space 3}0.000{col 54}{space 4} 1.154419{col 67}{space 3} 1.634338 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} 1.595241{col 26}{space 2} .119446{col 37}{space 1} 13.36{col 46}{space 3}0.000{col 54}{space 4} 1.359324{col 67}{space 3} 1.831158 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} 2.463391{col 26}{space 2} .1189512{col 37}{space 1} 20.71{col 46}{space 3}0.000{col 54}{space 4} 2.228451{col 67}{space 3} 2.698331 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} 5.987484{col 26}{space 2} .1200494{col 37}{space 1} 49.88{col 46}{space 3}0.000{col 54}{space 4} 5.750375{col 67}{space 3} 6.224593 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-3.637665{col 26}{space 2} .1119356{col 37}{space 1} -32.50{col 46}{space 3}0.000{col 54}{space 4}-3.858748{col 67}{space 3}-3.416582 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.5843435{col 26}{space 2} .1100077{col 37}{space 1} -5.31{col 46}{space 3}0.000{col 54}{space 4}-.8016189{col 67}{space 3}-.3670681 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0011646{col 26}{space 2} .1129425{col 37}{space 1} 0.01{col 46}{space 3}0.992{col 54}{space 4}-.2219072{col 67}{space 3} .2242365 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} 15.69559{col 26}{space 2} .2264428{col 37}{space 1} 69.31{col 46}{space 3}0.000{col 54}{space 4} 15.24834{col 67}{space 3} 16.14284 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 13.53745{col 26}{space 2} .2147977{col 37}{space 1} 63.02{col 46}{space 3}0.000{col 54}{space 4} 13.1132{col 67}{space 3} 13.96169 {txt}{space 10}f4 {c |}{col 14}{res}{space 2} 22.29717{col 26}{space 2} .2121213{col 37}{space 1} 105.12{col 46}{space 3}0.000{col 54}{space 4} 21.87821{col 67}{space 3} 22.71613 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} 3.036524{col 26}{space 2} .2222416{col 37}{space 1} 13.66{col 46}{space 3}0.000{col 54}{space 4} 2.597576{col 67}{space 3} 3.475472 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} 6.163013{col 26}{space 2} .1584812{col 37}{space 1} 38.89{col 46}{space 3}0.000{col 54}{space 4} 5.849998{col 67}{space 3} 6.476028 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} 5.602886{col 26}{space 2} .2072609{col 37}{space 1} 27.03{col 46}{space 3}0.000{col 54}{space 4} 5.193527{col 67}{space 3} 6.012246 {txt}{space 10}f8 {c |}{col 14}{res}{space 2} 12.29743{col 26}{space 2} .2087015{col 37}{space 1} 58.92{col 46}{space 3}0.000{col 54}{space 4} 11.88523{col 67}{space 3} 12.70963 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 18.47442{col 26}{space 2} .2111562{col 37}{space 1} 87.49{col 46}{space 3}0.000{col 54}{space 4} 18.05736{col 67}{space 3} 18.89147 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} -1.84336{col 26}{space 2} .2526039{col 37}{space 1} -7.30{col 46}{space 3}0.000{col 54}{space 4}-2.342276{col 67}{space 3}-1.344444 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} 5.614328{col 26}{space 2} .149034{col 37}{space 1} 37.67{col 46}{space 3}0.000{col 54}{space 4} 5.319972{col 67}{space 3} 5.908684 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} 8.255615{col 26}{space 2} .1371246{col 37}{space 1} 60.21{col 46}{space 3}0.000{col 54}{space 4} 7.984781{col 67}{space 3} 8.526448 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} 12.73322{col 26}{space 2} .1999435{col 37}{space 1} 63.68{col 46}{space 3}0.000{col 54}{space 4} 12.33831{col 67}{space 3} 13.12813 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} 58.88582{col 26}{space 2} 1.682346{col 37}{space 1} 35.00{col 46}{space 3}0.000{col 54}{space 4} 55.56304{col 67}{space 3} 62.20861 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 661 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.503 {res}First component regression {txt}(sum of wgt is 1.0075e+02) Linear regression Number of obs = {res} 200 {txt}F(28, 171) = {res} 81.63 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.8588 {txt}Root MSE = {res} .03342 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2}-.0114585{col 26}{space 2} .0012544{col 37}{space 1} -9.13{col 46}{space 3}0.000{col 54}{space 4}-.0139347{col 67}{space 3}-.0089824 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0232435{col 26}{space 2} .0041181{col 37}{space 1} -5.64{col 46}{space 3}0.000{col 54}{space 4}-.0313724{col 67}{space 3}-.0151145 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0802214{col 26}{space 2} .0143098{col 37}{space 1} -5.61{col 46}{space 3}0.000{col 54}{space 4} -.108468{col 67}{space 3}-.0519748 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0795868{col 26}{space 2} .0209865{col 37}{space 1} -3.79{col 46}{space 3}0.000{col 54}{space 4}-.1210127{col 67}{space 3}-.0381609 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.0892171{col 26}{space 2} .0152695{col 37}{space 1} -5.84{col 46}{space 3}0.000{col 54}{space 4}-.1193582{col 67}{space 3}-.0590761 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0694361{col 26}{space 2} .0135667{col 37}{space 1} -5.12{col 46}{space 3}0.000{col 54}{space 4}-.0962159{col 67}{space 3}-.0426563 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0112146{col 26}{space 2} .023121{col 37}{space 1} -0.49{col 46}{space 3}0.628{col 54}{space 4}-.0568538{col 67}{space 3} .0344247 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0026126{col 26}{space 2} .0183773{col 37}{space 1} 0.14{col 46}{space 3}0.887{col 54}{space 4} -.033663{col 67}{space 3} .0388883 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0191343{col 26}{space 2} .0147406{col 37}{space 1} 1.30{col 46}{space 3}0.196{col 54}{space 4}-.0099626{col 67}{space 3} .0482312 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0207515{col 26}{space 2} .0175646{col 37}{space 1} 1.18{col 46}{space 3}0.239{col 54}{space 4}-.0139199{col 67}{space 3} .0554228 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0078953{col 26}{space 2} .0224973{col 37}{space 1} 0.35{col 46}{space 3}0.726{col 54}{space 4}-.0365129{col 67}{space 3} .0523036 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.0154835{col 26}{space 2} .0181325{col 37}{space 1} -0.85{col 46}{space 3}0.394{col 54}{space 4}-.0512758{col 67}{space 3} .0203088 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0334984{col 26}{space 2} .0202178{col 37}{space 1} -1.66{col 46}{space 3}0.099{col 54}{space 4}-.0734071{col 67}{space 3} .0064102 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0050581{col 26}{space 2} .0137043{col 37}{space 1} 0.37{col 46}{space 3}0.713{col 54}{space 4}-.0219932{col 67}{space 3} .0321095 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0268305{col 26}{space 2} .0248769{col 37}{space 1} -1.08{col 46}{space 3}0.282{col 54}{space 4}-.0759359{col 67}{space 3} .0222749 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0172077{col 26}{space 2} .0158776{col 37}{space 1} -1.08{col 46}{space 3}0.280{col 54}{space 4} -.048549{col 67}{space 3} .0141336 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1179983{col 26}{space 2} .0179019{col 37}{space 1} -6.59{col 46}{space 3}0.000{col 54}{space 4}-.1533355{col 67}{space 3}-.0826611 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2077081{col 26}{space 2} .0204037{col 37}{space 1} -10.18{col 46}{space 3}0.000{col 54}{space 4}-.2479835{col 67}{space 3}-.1674326 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.0992808{col 26}{space 2} .0177956{col 37}{space 1} -5.58{col 46}{space 3}0.000{col 54}{space 4}-.1344081{col 67}{space 3}-.0641535 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.2326344{col 26}{space 2} .0153932{col 37}{space 1} -15.11{col 46}{space 3}0.000{col 54}{space 4}-.2630195{col 67}{space 3}-.2022493 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1564042{col 26}{space 2} .0178606{col 37}{space 1} -8.76{col 46}{space 3}0.000{col 54}{space 4}-.1916599{col 67}{space 3}-.1211485 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1622582{col 26}{space 2} .0150582{col 37}{space 1} -10.78{col 46}{space 3}0.000{col 54}{space 4}-.1919821{col 67}{space 3}-.1325344 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2620833{col 26}{space 2} .0163204{col 37}{space 1} -16.06{col 46}{space 3}0.000{col 54}{space 4}-.2942986{col 67}{space 3} -.229868 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} .0172719{col 26}{space 2} .0230013{col 37}{space 1} 0.75{col 46}{space 3}0.454{col 54}{space 4}-.0281311{col 67}{space 3} .0626748 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0123322{col 26}{space 2} .0161795{col 37}{space 1} -0.76{col 46}{space 3}0.447{col 54}{space 4}-.0442695{col 67}{space 3} .0196051 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.2228726{col 26}{space 2} .0181443{col 37}{space 1} -12.28{col 46}{space 3}0.000{col 54}{space 4}-.2586882{col 67}{space 3} -.187057 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -.06289{col 26}{space 2} .0179739{col 37}{space 1} -3.50{col 46}{space 3}0.001{col 54}{space 4}-.0983692{col 67}{space 3}-.0274108 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.2712316{col 26}{space 2} .0151357{col 37}{space 1} -17.92{col 46}{space 3}0.000{col 54}{space 4}-.3011085{col 67}{space 3}-.2413547 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3175237{col 26}{space 2} .0199743{col 37}{space 1} 15.90{col 46}{space 3}0.000{col 54}{space 4} .2780956{col 67}{space 3} .3569517 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 9.9255e+01) Linear regression Number of obs = {res} 180 {txt}F(28, 151) > {res} 99999.00 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.6831 {txt}Root MSE = {res} .07562 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0159825{col 26}{space 2} .0030279{col 37}{space 1} 5.28{col 46}{space 3}0.000{col 54}{space 4} .0100001{col 67}{space 3} .021965 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0073664{col 26}{space 2} .0148632{col 37}{space 1} -0.50{col 46}{space 3}0.621{col 54}{space 4} -.036733{col 67}{space 3} .0220003 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .091394{col 26}{space 2} .0271661{col 37}{space 1} 3.36{col 46}{space 3}0.001{col 54}{space 4} .0377193{col 67}{space 3} .1450687 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0282401{col 26}{space 2} .0277249{col 37}{space 1} 1.02{col 46}{space 3}0.310{col 54}{space 4}-.0265388{col 67}{space 3} .0830189 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0158877{col 26}{space 2} .0342143{col 37}{space 1} 0.46{col 46}{space 3}0.643{col 54}{space 4}-.0517129{col 67}{space 3} .0834882 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .0472548{col 26}{space 2} .0298569{col 37}{space 1} 1.58{col 46}{space 3}0.116{col 54}{space 4}-.0117365{col 67}{space 3} .1062461 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .0018715{col 26}{space 2} .0255631{col 37}{space 1} 0.07{col 46}{space 3}0.942{col 54}{space 4} -.048636{col 67}{space 3} .0523791 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .3057873{col 26}{space 2} .0452768{col 37}{space 1} 6.75{col 46}{space 3}0.000{col 54}{space 4} .2163294{col 67}{space 3} .3952452 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .4378011{col 26}{space 2} .0418522{col 37}{space 1} 10.46{col 46}{space 3}0.000{col 54}{space 4} .3551096{col 67}{space 3} .5204927 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0459054{col 26}{space 2} .0388614{col 37}{space 1} 1.18{col 46}{space 3}0.239{col 54}{space 4}-.0308768{col 67}{space 3} .1226877 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0766538{col 26}{space 2} .0294935{col 37}{space 1} 2.60{col 46}{space 3}0.010{col 54}{space 4} .0183805{col 67}{space 3} .1349271 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .1104019{col 26}{space 2} .0261009{col 37}{space 1} 4.23{col 46}{space 3}0.000{col 54}{space 4} .0588318{col 67}{space 3} .161972 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .2238767{col 26}{space 2} .0453725{col 37}{space 1} 4.93{col 46}{space 3}0.000{col 54}{space 4} .1342298{col 67}{space 3} .3135235 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0189597{col 26}{space 2} .035923{col 37}{space 1} 0.53{col 46}{space 3}0.598{col 54}{space 4}-.0520168{col 67}{space 3} .0899363 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .054784{col 26}{space 2} .0260734{col 37}{space 1} 2.10{col 46}{space 3}0.037{col 54}{space 4} .0032681{col 67}{space 3} .1062999 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0429776{col 26}{space 2} .0362366{col 37}{space 1} 1.19{col 46}{space 3}0.237{col 54}{space 4}-.0286186{col 67}{space 3} .1145738 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1508857{col 26}{space 2} .0427908{col 37}{space 1} -3.53{col 46}{space 3}0.001{col 54}{space 4}-.2354317{col 67}{space 3}-.0663398 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2085035{col 26}{space 2} .0416891{col 37}{space 1} -5.00{col 46}{space 3}0.000{col 54}{space 4}-.2908728{col 67}{space 3}-.1261343 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1084315{col 26}{space 2} .0583954{col 37}{space 1} -1.86{col 46}{space 3}0.065{col 54}{space 4}-.2238092{col 67}{space 3} .0069461 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1978614{col 26}{space 2} .0454575{col 37}{space 1} -4.35{col 46}{space 3}0.000{col 54}{space 4}-.2876763{col 67}{space 3}-.1080465 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .0713939{col 26}{space 2} .0530448{col 37}{space 1} 1.35{col 46}{space 3}0.180{col 54}{space 4}-.0334119{col 67}{space 3} .1761997 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1327934{col 26}{space 2} .0485092{col 37}{space 1} -2.74{col 46}{space 3}0.007{col 54}{space 4}-.2286378{col 67}{space 3} -.036949 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1922588{col 26}{space 2} .0400537{col 37}{space 1} -4.80{col 46}{space 3}0.000{col 54}{space 4}-.2713969{col 67}{space 3}-.1131207 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1345265{col 26}{space 2} .0447873{col 37}{space 1} -3.00{col 46}{space 3}0.003{col 54}{space 4}-.2230172{col 67}{space 3}-.0460358 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.2139416{col 26}{space 2} .0447045{col 37}{space 1} -4.79{col 46}{space 3}0.000{col 54}{space 4}-.3022688{col 67}{space 3}-.1256145 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} -.10143{col 26}{space 2} .0460079{col 37}{space 1} -2.20{col 46}{space 3}0.029{col 54}{space 4}-.1923324{col 67}{space 3}-.0105276 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .0339231{col 26}{space 2} .0910909{col 37}{space 1} 0.37{col 46}{space 3}0.710{col 54}{space 4}-.1460542{col 67}{space 3} .2139004 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.0985424{col 26}{space 2} .0451246{col 37}{space 1} -2.18{col 46}{space 3}0.031{col 54}{space 4}-.1876995{col 67}{space 3}-.0093854 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1772714{col 26}{space 2} .0460836{col 37}{space 1} 3.85{col 46}{space 3}0.000{col 54}{space 4} .0862195{col 67}{space 3} .2683233 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(215 real changes made) {res}This Switching Regression took {com}26{res} seconds. {txt}(199 real changes made, 23 to missing) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00072 Average of the probability vector is: {txt}0.451 On iteration {com}50{txt} greatest diff is: {com}-0.001769 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 302.90251 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00085 Average of the probability vector is: {txt}0.451 On iteration {com}100{txt} greatest diff is: {com}-0.005675 on yr4 in the{txt} first main {com}eqn {txt}Log-likelihood is : 304.65844 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00024 Average of the probability vector is: {txt}0.454 On iteration {com}150{txt} greatest diff is: {com}0.003072 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.10074 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00012 Average of the probability vector is: {txt}0.456 On iteration {com}200{txt} greatest diff is: {com}0.021492 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.30032 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00006 Average of the probability vector is: {txt}0.457 On iteration {com}250{txt} greatest diff is: {com}0.004931 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.44866 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00005 Average of the probability vector is: {txt}0.457 On iteration {com}300{txt} greatest diff is: {com}0.002290 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.57415 {res}This iteration took {com}1{res} second. On iter 350 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.457 On iteration {com}350{txt} greatest diff is: {com}0.001530 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.67365 {res}This iteration took {com}1{res} second. On iter 400 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.458 On iteration {com}400{txt} greatest diff is: {com}0.001174 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.7494 {res}This iteration took {com}0{res} second. On iter 450 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.459 On iteration {com}450{txt} greatest diff is: {com}0.000838 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.80594 {res}This iteration took {com}0{res} second. On iter 500 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.459 On iteration {com}500{txt} greatest diff is: {com}0.000366 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.84949 {res}This iteration took {com}0{res} second. On iter 550 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.460 On iteration {com}550{txt} greatest diff is: {com}0.000298 on yr13 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.88772 {res}This iteration took {com}0{res} second. On iter 600 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.461 On iteration {com}600{txt} greatest diff is: {com}-0.000281 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.92607 {res}This iteration took {com}0{res} second. On iter 650 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.461 On iteration {com}650{txt} greatest diff is: {com}-0.000162 on yr10 in the{txt} second main {com}eqn {txt}Log-likelihood is : 305.96414 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 4.90e-24 2.74e-48 {txt} 5% {res} 4.36e-12 1.41e-32 {txt}10% {res} 2.04e-09 4.90e-24 {txt}Obs {res} 201 {txt}25% {res} .0030934 1.19e-19 {txt}Sum of Wgt. {res} 201 {txt}50% {res} .481635 {txt}Mean {res} .460969 {txt}Largest Std. Dev. {res} .4093335 {txt}75% {res} .9042655 .9999994 {txt}90% {res} .9908247 .9999999 {txt}Variance {res} .1675539 {txt}95% {res} .9996998 .9999999 {txt}Skewness {res} .0728075 {txt}99% {res} .9999999 1 {txt}Kurtosis {res} 1.293306 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 201 {txt}{hline 13}{c +}{hline 34} F(31, 169) = {res} 339.01 {txt} Model {c |} {res} 2136.05885 31 68.9051241 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 34.3495074 169 .203251523 {txt}R-squared ={res} 0.9842 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9813 {txt} Total {c |} {res} 2170.40836 200 10.8520418 {txt}Root MSE = {res} .45083 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 6}kz_cfs {c |}{col 14}{res}{space 2} 1.064433{col 26}{space 2} .0326644{col 37}{space 1} 32.59{col 46}{space 3}0.000{col 54}{space 4} .9999507{col 67}{space 3} 1.128916 {txt}{space 9}mtb {c |}{col 14}{res}{space 2} -.00547{col 26}{space 2} .0042646{col 37}{space 1} -1.28{col 46}{space 3}0.201{col 54}{space 4}-.0138888{col 67}{space 3} .0029488 {txt}{space 8}kz_d {c |}{col 14}{res}{space 2} 2.489367{col 26}{space 2} .2599182{col 37}{space 1} 9.58{col 46}{space 3}0.000{col 54}{space 4} 1.976262{col 67}{space 3} 3.002471 {txt}{space 6}kz_div {c |}{col 14}{res}{space 2}-10.59866{col 26}{space 2} .3021359{col 37}{space 1} -35.08{col 46}{space 3}0.000{col 54}{space 4}-11.19511{col 67}{space 3}-10.00221 {txt}{space 7}kz_cs {c |}{col 14}{res}{space 2} .6963734{col 26}{space 2} .0967171{col 37}{space 1} 7.20{col 46}{space 3}0.000{col 54}{space 4} .5054441{col 67}{space 3} .8873027 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-1.444841{col 26}{space 2} .218285{col 37}{space 1} -6.62{col 46}{space 3}0.000{col 54}{space 4}-1.875758{col 67}{space 3}-1.013925 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .6568674{col 26}{space 2} .1733765{col 37}{space 1} 3.79{col 46}{space 3}0.000{col 54}{space 4} .3146048{col 67}{space 3} .9991299 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.6591195{col 26}{space 2} .1632275{col 37}{space 1} -4.04{col 46}{space 3}0.000{col 54}{space 4} -.981347{col 67}{space 3} -.336892 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} 3.743008{col 26}{space 2} .166402{col 37}{space 1} 22.49{col 46}{space 3}0.000{col 54}{space 4} 3.414513{col 67}{space 3} 4.071502 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-4.750071{col 26}{space 2} .1614282{col 37}{space 1} -29.43{col 46}{space 3}0.000{col 54}{space 4}-5.068746{col 67}{space 3}-4.431395 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-3.137559{col 26}{space 2} .1533493{col 37}{space 1} -20.46{col 46}{space 3}0.000{col 54}{space 4}-3.440286{col 67}{space 3}-2.834832 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-2.804796{col 26}{space 2} .153226{col 37}{space 1} -18.30{col 46}{space 3}0.000{col 54}{space 4}-3.107279{col 67}{space 3}-2.502312 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.5993471{col 26}{space 2} .1515434{col 37}{space 1} -3.95{col 46}{space 3}0.000{col 54}{space 4} -.898509{col 67}{space 3}-.3001852 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} -.952986{col 26}{space 2} .1499994{col 37}{space 1} -6.35{col 46}{space 3}0.000{col 54}{space 4} -1.2491{col 67}{space 3} -.656872 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2}-.4824482{col 26}{space 2} .1500185{col 37}{space 1} -3.22{col 46}{space 3}0.002{col 54}{space 4}-.7785998{col 67}{space 3}-.1862966 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-2.134945{col 26}{space 2} .1501413{col 37}{space 1} -14.22{col 46}{space 3}0.000{col 54}{space 4}-2.431339{col 67}{space 3}-1.838551 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.7018921{col 26}{space 2} .1495133{col 37}{space 1} -4.69{col 46}{space 3}0.000{col 54}{space 4}-.9970465{col 67}{space 3}-.4067377 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .5725779{col 26}{space 2} .1490104{col 37}{space 1} 3.84{col 46}{space 3}0.000{col 54}{space 4} .2784164{col 67}{space 3} .8667394 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.1728954{col 26}{space 2} .1546482{col 37}{space 1} -1.12{col 46}{space 3}0.265{col 54}{space 4}-.4781865{col 67}{space 3} .1323958 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-2.248858{col 26}{space 2} .199999{col 37}{space 1} -11.24{col 46}{space 3}0.000{col 54}{space 4}-2.643676{col 67}{space 3}-1.854039 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} 2.764044{col 26}{space 2} .2113443{col 37}{space 1} 13.08{col 46}{space 3}0.000{col 54}{space 4} 2.346829{col 67}{space 3} 3.181259 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-3.951657{col 26}{space 2} .1920301{col 37}{space 1} -20.58{col 46}{space 3}0.000{col 54}{space 4}-4.330744{col 67}{space 3} -3.57257 {txt}{space 10}f5 {c |}{col 14}{res}{space 2} .9112013{col 26}{space 2} .1991{col 37}{space 1} 4.58{col 46}{space 3}0.000{col 54}{space 4} .5181579{col 67}{space 3} 1.304245 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-2.057477{col 26}{space 2} .1678415{col 37}{space 1} -12.26{col 46}{space 3}0.000{col 54}{space 4}-2.388813{col 67}{space 3} -1.72614 {txt}{space 10}f7 {c |}{col 14}{res}{space 2} .3771187{col 26}{space 2} .1713602{col 37}{space 1} 2.20{col 46}{space 3}0.029{col 54}{space 4} .0388365{col 67}{space 3} .7154008 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.7010693{col 26}{space 2} .1735741{col 37}{space 1} -4.04{col 46}{space 3}0.000{col 54}{space 4}-1.043722{col 67}{space 3}-.3584166 {txt}{space 10}f9 {c |}{col 14}{res}{space 2} 1.39205{col 26}{space 2} .2059075{col 37}{space 1} 6.76{col 46}{space 3}0.000{col 54}{space 4} .9855682{col 67}{space 3} 1.798533 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-1.820327{col 26}{space 2} .2518147{col 37}{space 1} -7.23{col 46}{space 3}0.000{col 54}{space 4}-2.317434{col 67}{space 3}-1.323219 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .5005971{col 26}{space 2} .1804722{col 37}{space 1} 2.77{col 46}{space 3}0.006{col 54}{space 4} .1443268{col 67}{space 3} .8568673 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-1.052307{col 26}{space 2} .1877817{col 37}{space 1} -5.60{col 46}{space 3}0.000{col 54}{space 4}-1.423007{col 67}{space 3}-.6816075 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-1.461277{col 26}{space 2} .1781246{col 37}{space 1} -8.20{col 46}{space 3}0.000{col 54}{space 4}-1.812913{col 67}{space 3}-1.109641 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.176028{col 26}{space 2} .1645192{col 37}{space 1} 7.15{col 46}{space 3}0.000{col 54}{space 4} .8512503{col 67}{space 3} 1.500805 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 670 the mean absolute change in the probability vector is : 0.00001 Average of the probability vector is: {txt}0.461 {res}First component regression {txt}(sum of wgt is 9.3144e+01) Linear regression Number of obs = {res} 201 {txt}{help j_robustsingular:F(27, 172) } = {res} . {txt}Prob > F = {res} . {txt}R-squared = {res} 0.8044 {txt}Root MSE = {res} .05605 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0179056{col 26}{space 2} .0029163{col 37}{space 1} 6.14{col 46}{space 3}0.000{col 54}{space 4} .0121492{col 67}{space 3} .0236619 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0579378{col 26}{space 2} .0256717{col 37}{space 1} 2.26{col 46}{space 3}0.025{col 54}{space 4} .0072655{col 67}{space 3} .10861 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} .1330027{col 26}{space 2} .02194{col 37}{space 1} 6.06{col 46}{space 3}0.000{col 54}{space 4} .0896964{col 67}{space 3} .176309 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0491188{col 26}{space 2} .023063{col 37}{space 1} 2.13{col 46}{space 3}0.035{col 54}{space 4} .0035959{col 67}{space 3} .0946417 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .0273301{col 26}{space 2} .0257662{col 37}{space 1} 1.06{col 46}{space 3}0.290{col 54}{space 4}-.0235285{col 67}{space 3} .0781888 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2} .0507703{col 26}{space 2} .0256471{col 37}{space 1} 1.98{col 46}{space 3}0.049{col 54}{space 4} .0001467{col 67}{space 3} .1013938 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} .0404556{col 26}{space 2} .0165431{col 37}{space 1} 2.45{col 46}{space 3}0.015{col 54}{space 4} .0078019{col 67}{space 3} .0731093 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .3360824{col 26}{space 2} .0314409{col 37}{space 1} 10.69{col 46}{space 3}0.000{col 54}{space 4} .2740226{col 67}{space 3} .3981421 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .4116417{col 26}{space 2} .0349397{col 37}{space 1} 11.78{col 46}{space 3}0.000{col 54}{space 4} .3426758{col 67}{space 3} .4806075 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .04928{col 26}{space 2} .0298238{col 37}{space 1} 1.65{col 46}{space 3}0.100{col 54}{space 4}-.0095878{col 67}{space 3} .1081478 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0931715{col 26}{space 2} .0263134{col 37}{space 1} 3.54{col 46}{space 3}0.001{col 54}{space 4} .0412327{col 67}{space 3} .1451103 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .1085602{col 26}{space 2} .0219854{col 37}{space 1} 4.94{col 46}{space 3}0.000{col 54}{space 4} .0651643{col 67}{space 3} .1519561 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .230413{col 26}{space 2} .0340816{col 37}{space 1} 6.76{col 46}{space 3}0.000{col 54}{space 4} .163141{col 67}{space 3} .2976851 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .030203{col 26}{space 2} .0176237{col 37}{space 1} 1.71{col 46}{space 3}0.088{col 54}{space 4}-.0045835{col 67}{space 3} .0649896 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2} .0637265{col 26}{space 2} .0203077{col 37}{space 1} 3.14{col 46}{space 3}0.002{col 54}{space 4} .0236421{col 67}{space 3} .1038108 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0678782{col 26}{space 2} .0361005{col 37}{space 1} 1.88{col 46}{space 3}0.062{col 54}{space 4} -.003379{col 67}{space 3} .1391353 {txt}{space 10}f2 {c |}{col 14}{res}{space 2} -.149927{col 26}{space 2} .0350531{col 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{c |}{col 14}{res}{space 2}-.1673117{col 26}{space 2} .0307154{col 37}{space 1} -5.45{col 46}{space 3}0.000{col 54}{space 4}-.2279393{col 67}{space 3}-.1066841 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1469547{col 26}{space 2} .0372039{col 37}{space 1} -3.95{col 46}{space 3}0.000{col 54}{space 4}-.2203897{col 67}{space 3}-.0735198 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.1709916{col 26}{space 2} .0392885{col 37}{space 1} -4.35{col 46}{space 3}0.000{col 54}{space 4}-.2485413{col 67}{space 3}-.0934419 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0835612{col 26}{space 2} .0354682{col 37}{space 1} -2.36{col 46}{space 3}0.020{col 54}{space 4}-.1535702{col 67}{space 3}-.0135522 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.1491097{col 26}{space 2} .039086{col 37}{space 1} -3.81{col 46}{space 3}0.000{col 54}{space 4}-.2262598{col 67}{space 3}-.0719597 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.0647151{col 26}{space 2} .0368749{col 37}{space 1} -1.75{col 46}{space 3}0.081{col 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.0248551{col 37}{space 1} -6.97{col 46}{space 3}0.000{col 54}{space 4}-.2224254{col 67}{space 3}-.1242926 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.2247616{col 26}{space 2} .0252677{col 37}{space 1} -8.90{col 46}{space 3}0.000{col 54}{space 4}-.2746426{col 67}{space 3}-.1748805 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1564393{col 26}{space 2} .0235337{col 37}{space 1} -6.65{col 46}{space 3}0.000{col 54}{space 4}-.2028973{col 67}{space 3}-.1099814 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.2502851{col 26}{space 2} .0226818{col 37}{space 1} -11.03{col 46}{space 3}0.000{col 54}{space 4}-.2950613{col 67}{space 3}-.2055089 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.1866431{col 26}{space 2} .0231916{col 37}{space 1} -8.05{col 46}{space 3}0.000{col 54}{space 4}-.2324256{col 67}{space 3}-.1408606 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1599738{col 26}{space 2} .0265184{col 37}{space 1} -6.03{col 46}{space 3}0.000{col 54}{space 4}-.2123237{col 67}{space 3}-.1076239 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2850909{col 26}{space 2} .0224104{col 37}{space 1} -12.72{col 46}{space 3}0.000{col 54}{space 4}-.3293312{col 67}{space 3}-.2408506 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.0358584{col 26}{space 2} .0296343{col 37}{space 1} -1.21{col 46}{space 3}0.228{col 54}{space 4}-.0943595{col 67}{space 3} .0226427 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0646843{col 26}{space 2} .0228041{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1097019{col 67}{space 3}-.0196667 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.2492091{col 26}{space 2} .0244969{col 37}{space 1} -10.17{col 46}{space 3}0.000{col 54}{space 4}-.2975685{col 67}{space 3}-.2008497 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -.065841{col 26}{space 2} .0262928{col 37}{space 1} -2.50{col 46}{space 3}0.013{col 54}{space 4}-.1177455{col 67}{space 3}-.0139364 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} -.297188{col 26}{space 2} .0213377{col 37}{space 1} -13.93{col 46}{space 3}0.000{col 54}{space 4}-.3393107{col 67}{space 3}-.2550654 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3860413{col 26}{space 2} .0270106{col 37}{space 1} 14.29{col 46}{space 3}0.000{col 54}{space 4} .3327197{col 67}{space 3} .4393629 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(221 real changes made) {res}This Switching Regression took {com}27{res} seconds. {txt}(0 real changes made) {res}Here is the regression for the full sample. {txt} Source {c |} SS df MS Number of obs ={res} 207 {txt}{hline 13}{c +}{hline 34} F(28, 178) = {res} 4.11 {txt} Model {c |} {res} .945627783 28 .033772421 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.46211163 178 .00821411 {txt}R-squared ={res} 0.3927 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2972 {txt} Total {c |} {res} 2.40773942 206 .011688055 {txt}Root MSE = {res} .09063 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2} .0047578{col 26}{space 2} .0028822{col 37}{space 1} 1.65{col 46}{space 3}0.101{col 54}{space 4}-.0009299{col 67}{space 3} .0104454 {txt}{space 9}cfs {c |}{col 14}{res}{space 2} .0116013{col 26}{space 2} .0122208{col 37}{space 1} 0.95{col 46}{space 3}0.344{col 54}{space 4} -.012515{col 67}{space 3} .0357177 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.0719173{col 26}{space 2} .0364159{col 37}{space 1} -1.97{col 46}{space 3}0.050{col 54}{space 4}-.1437798{col 67}{space 3}-.0000548 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.0549636{col 26}{space 2} .0314186{col 37}{space 1} -1.75{col 46}{space 3}0.082{col 54}{space 4}-.1169646{col 67}{space 3} .0070373 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} -.041225{col 26}{space 2} .0316303{col 37}{space 1} -1.30{col 46}{space 3}0.194{col 54}{space 4}-.1036437{col 67}{space 3} .0211937 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.0055385{col 26}{space 2} .0327339{col 37}{space 1} -0.17{col 46}{space 3}0.866{col 54}{space 4} -.070135{col 67}{space 3} .059058 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} -.008974{col 26}{space 2} .0325852{col 37}{space 1} -0.28{col 46}{space 3}0.783{col 54}{space 4} -.073277{col 67}{space 3} .0553289 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0039962{col 26}{space 2} .0311555{col 37}{space 1} 0.13{col 46}{space 3}0.898{col 54}{space 4}-.0574853{col 67}{space 3} .0654778 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .0461105{col 26}{space 2} .0304342{col 37}{space 1} 1.52{col 46}{space 3}0.132{col 54}{space 4}-.0139478{col 67}{space 3} .1061688 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2} .0170913{col 26}{space 2} .0305374{col 37}{space 1} 0.56{col 46}{space 3}0.576{col 54}{space 4}-.0431706{col 67}{space 3} .0773532 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} .0259821{col 26}{space 2} .0300568{col 37}{space 1} 0.86{col 46}{space 3}0.389{col 54}{space 4}-.0333314{col 67}{space 3} .0852956 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .0239012{col 26}{space 2} .0300488{col 37}{space 1} 0.80{col 46}{space 3}0.427{col 54}{space 4}-.0353965{col 67}{space 3} .0831989 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} .0155031{col 26}{space 2} .0299422{col 37}{space 1} 0.52{col 46}{space 3}0.605{col 54}{space 4}-.0435842{col 67}{space 3} .0745905 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-.0009178{col 26}{space 2} .0296753{col 37}{space 1} -0.03{col 46}{space 3}0.975{col 54}{space 4}-.0594785{col 67}{space 3} .0576429 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.0068683{col 26}{space 2} .030254{col 37}{space 1} -0.23{col 46}{space 3}0.821{col 54}{space 4} -.066571{col 67}{space 3} .0528344 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2} .0144013{col 26}{space 2} .0313182{col 37}{space 1} 0.46{col 46}{space 3}0.646{col 54}{space 4}-.0474015{col 67}{space 3} .0762041 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1419005{col 26}{space 2} .0323357{col 37}{space 1} -4.39{col 46}{space 3}0.000{col 54}{space 4}-.2057113{col 67}{space 3}-.0780898 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1763313{col 26}{space 2} .0333742{col 37}{space 1} -5.28{col 46}{space 3}0.000{col 54}{space 4}-.2421914{col 67}{space 3}-.1104712 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.1400866{col 26}{space 2} .033434{col 37}{space 1} -4.19{col 46}{space 3}0.000{col 54}{space 4}-.2060646{col 67}{space 3}-.0741086 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1699417{col 26}{space 2} .0344004{col 37}{space 1} -4.94{col 46}{space 3}0.000{col 54}{space 4}-.2378267{col 67}{space 3}-.1020566 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.0684102{col 26}{space 2} .0308518{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1292926{col 67}{space 3}-.0075277 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1096345{col 26}{space 2} .031518{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1718316{col 67}{space 3}-.0474375 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.2015983{col 26}{space 2} .0325413{col 37}{space 1} -6.20{col 46}{space 3}0.000{col 54}{space 4}-.2658146{col 67}{space 3} -.137382 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1031833{col 26}{space 2} .0363427{col 37}{space 1} -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1749012{col 67}{space 3}-.0314653 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0285306{col 26}{space 2} .0366215{col 37}{space 1} -0.78{col 46}{space 3}0.437{col 54}{space 4}-.1007988{col 67}{space 3} .0437375 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.1538928{col 26}{space 2} .0311871{col 37}{space 1} -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2154369{col 67}{space 3}-.0923487 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0491726{col 26}{space 2} .0311886{col 37}{space 1} -1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107196{col 67}{space 3} .0123743 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.1693859{col 26}{space 2} .0312185{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4} -.230992{col 67}{space 3}-.1077799 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2362087{col 26}{space 2} .0260063{col 37}{space 1} 9.08{col 46}{space 3}0.000{col 54}{space 4} .1848884{col 67}{space 3} .287529 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(Reporting of crit for first iteration skipped) {res}On iter 50 the mean absolute change in the probability vector is : 0.00109 Average of the probability vector is: {txt}0.423 On iteration {com}50{txt} greatest diff is: {com}-0.003888 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 312.23548 {res}This iteration took {com}0{res} second. On iter 100 the mean absolute change in the probability vector is : 0.00018 Average of the probability vector is: {txt}0.418 On iteration {com}100{txt} greatest diff is: {com}0.001018 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 313.85799 {res}This iteration took {com}0{res} second. On iter 150 the mean absolute change in the probability vector is : 0.00008 Average of the probability vector is: {txt}0.418 On iteration {com}150{txt} greatest diff is: {com}0.000472 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 314.28672 {res}This iteration took {com}0{res} second. On iter 200 the mean absolute change in the probability vector is : 0.00004 Average of the probability vector is: {txt}0.418 On iteration {com}200{txt} greatest diff is: {com}0.000244 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 314.47047 {res}This iteration took {com}0{res} second. On iter 250 the mean absolute change in the probability vector is : 0.00003 Average of the probability vector is: {txt}0.418 On iteration {com}250{txt} greatest diff is: {com}0.000148 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 314.56915 {res}This iteration took {com}0{res} second. On iter 300 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.417 On iteration {com}300{txt} greatest diff is: {com}0.000101 on yr7 in the{txt} first main {com}eqn {txt}Log-likelihood is : 314.62968 {res}This iteration took {com}0{res} second. {txt}Done ...{com}Here are the probabilities of being in the first component regression.... {txt}__00000D {hline 61} Percentiles Smallest 1% {res} 2.73e-20 3.14e-22 {txt} 5% {res} 3.74e-15 1.32e-20 {txt}10% {res} 6.39e-06 2.73e-20 {txt}Obs {res} 203 {txt}25% {res} .0197576 3.76e-19 {txt}Sum of Wgt. {res} 203 {txt}50% {res} .330725 {txt}Mean {res} .4174493 {txt}Largest Std. Dev. {res} .3810895 {txt}75% {res} .7897983 1 {txt}90% {res} .99071 1 {txt}Variance {res} .1452292 {txt}95% {res} .9999999 1 {txt}Skewness {res} .3237458 {txt}99% {res} 1 1 {txt}Kurtosis {res} 1.52728 Final Results Follow Here is the regression for the switching eq'n {txt} Source {c |} SS df MS Number of obs ={res} 203 {txt}{hline 13}{c +}{hline 34} F(32, 170) = {res} 261.16 {txt} Model {c |} {res} 2011.19992 32 62.8499976 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 40.9119343 170 .240658437 {txt}R-squared ={res} 0.9801 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9763 {txt} Total {c |} {res} 2052.11186 202 10.1589696 {txt}Root MSE = {res} .49057 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} di{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 8}ww_d {c |}{col 14}{res}{space 2} 2.830926{col 26}{space 2} .2800726{col 37}{space 1} 10.11{col 46}{space 3}0.000{col 54}{space 4} 2.278058{col 67}{space 3} 3.383794 {txt}{space 6}ww_div {c |}{col 14}{res}{space 2}-.2458612{col 26}{space 2} .1293147{col 37}{space 1} -1.90{col 46}{space 3}0.059{col 54}{space 4}-.5011305{col 67}{space 3} .0094081 {txt}{space 3}ww_gsales {c |}{col 14}{res}{space 2} 1.053659{col 26}{space 2} .1732479{col 37}{space 1} 6.08{col 46}{space 3}0.000{col 54}{space 4} .7116645{col 67}{space 3} 1.395653 {txt}{space 5}ww_size {c |}{col 14}{res}{space 2}-.6752344{col 26}{space 2} .1001143{col 37}{space 1} -6.74{col 46}{space 3}0.000{col 54}{space 4}-.8728617{col 67}{space 3}-.4776072 {txt}{space 7}ww_cs {c |}{col 14}{res}{space 2}-3.856237{col 26}{space 2} .3573843{col 37}{space 1} -10.79{col 46}{space 3}0.000{col 54}{space 4} -4.56172{col 67}{space 3}-3.150754 {txt}{space 6}ww_cfs {c |}{col 14}{res}{space 2} 3.153651{col 26}{space 2} .2941267{col 37}{space 1} 10.72{col 46}{space 3}0.000{col 54}{space 4} 2.57304{col 67}{space 3} 3.734262 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2} 1.435257{col 26}{space 2} .2082625{col 37}{space 1} 6.89{col 46}{space 3}0.000{col 54}{space 4} 1.024144{col 67}{space 3} 1.846371 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2} .0643574{col 26}{space 2} .1745857{col 37}{space 1} 0.37{col 46}{space 3}0.713{col 54}{space 4}-.2802777{col 67}{space 3} .4089926 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2} .3933536{col 26}{space 2} .1684066{col 37}{space 1} 2.34{col 46}{space 3}0.021{col 54}{space 4} .0609163{col 67}{space 3} .725791 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.7401748{col 26}{space 2} .1755105{col 37}{space 1} -4.22{col 46}{space 3}0.000{col 54}{space 4}-1.086635{col 67}{space 3}-.3937142 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2} 4.67274{col 26}{space 2} .1792075{col 37}{space 1} 26.07{col 46}{space 3}0.000{col 54}{space 4} 4.318981{col 67}{space 3} 5.026498 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2}-1.426271{col 26}{space 2} .1764826{col 37}{space 1} -8.08{col 46}{space 3}0.000{col 54}{space 4} -1.77465{col 67}{space 3}-1.077891 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2}-1.479854{col 26}{space 2} .1685507{col 37}{space 1} -8.78{col 46}{space 3}0.000{col 54}{space 4}-1.812576{col 67}{space 3}-1.147132 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-1.855263{col 26}{space 2} .1719102{col 37}{space 1} -10.79{col 46}{space 3}0.000{col 54}{space 4}-2.194616{col 67}{space 3}-1.515909 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2}-.1790236{col 26}{space 2} .1652867{col 37}{space 1} -1.08{col 46}{space 3}0.280{col 54}{space 4}-.5053023{col 67}{space 3} .1472552 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} .5395176{col 26}{space 2} .1665691{col 37}{space 1} 3.24{col 46}{space 3}0.001{col 54}{space 4} .2107073{col 67}{space 3} .8683278 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2} 5.317097{col 26}{space 2} .1625428{col 37}{space 1} 32.71{col 46}{space 3}0.000{col 54}{space 4} 4.996234{col 67}{space 3} 5.637959 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2}-1.394777{col 26}{space 2} .1651931{col 37}{space 1} -8.44{col 46}{space 3}0.000{col 54}{space 4}-1.720871{col 67}{space 3}-1.068683 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-1.244606{col 26}{space 2} .1643669{col 37}{space 1} -7.57{col 46}{space 3}0.000{col 54}{space 4}-1.569069{col 67}{space 3}-.9201432 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.2094848{col 26}{space 2} .1678379{col 37}{space 1} -1.25{col 46}{space 3}0.214{col 54}{space 4}-.5407996{col 67}{space 3} .1218299 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.1576587{col 26}{space 2} .2389024{col 37}{space 1} -0.66{col 46}{space 3}0.510{col 54}{space 4}-.6292561{col 67}{space 3} .3139387 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-2.034354{col 26}{space 2} .247694{col 37}{space 1} -8.21{col 46}{space 3}0.000{col 54}{space 4}-2.523306{col 67}{space 3}-1.545402 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-1.183556{col 26}{space 2} .2093552{col 37}{space 1} -5.65{col 46}{space 3}0.000{col 54}{space 4}-1.596827{col 67}{space 3}-.7702853 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-8.879415{col 26}{space 2} .2563975{col 37}{space 1} -34.63{col 46}{space 3}0.000{col 54}{space 4}-9.385548{col 67}{space 3}-8.373282 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .5289506{col 26}{space 2} .2288478{col 37}{space 1} 2.31{col 46}{space 3}0.022{col 54}{space 4} .0772011{col 67}{space 3} .9807 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-1.198959{col 26}{space 2} .2206499{col 37}{space 1} -5.43{col 46}{space 3}0.000{col 54}{space 4}-1.634525{col 67}{space 3}-.7633922 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-3.664301{col 26}{space 2} .1978442{col 37}{space 1} -18.52{col 46}{space 3}0.000{col 54}{space 4}-4.054849{col 67}{space 3}-3.273753 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-1.544578{col 26}{space 2} .2496452{col 37}{space 1} -6.19{col 46}{space 3}0.000{col 54}{space 4}-2.037381{col 67}{space 3}-1.051774 {txt}{space 9}f10 {c |}{col 14}{res}{space 2} .9318502{col 26}{space 2} .3135066{col 37}{space 1} 2.97{col 46}{space 3}0.003{col 54}{space 4} .312983{col 67}{space 3} 1.550717 {txt}{space 9}f11 {c |}{col 14}{res}{space 2} .7226967{col 26}{space 2} .2121119{col 37}{space 1} 3.41{col 46}{space 3}0.001{col 54}{space 4} .3039843{col 67}{space 3} 1.141409 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} .8814512{col 26}{space 2} .2119027{col 37}{space 1} 4.16{col 46}{space 3}0.000{col 54}{space 4} .4631518{col 67}{space 3} 1.299751 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} .527753{col 26}{space 2} .2056906{col 37}{space 1} 2.57{col 46}{space 3}0.011{col 54}{space 4} .1217163{col 67}{space 3} .9337897 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.215822{col 26}{space 2} 1.137617{col 37}{space 1} 6.34{col 46}{space 3}0.000{col 54}{space 4} 4.970148{col 67}{space 3} 9.461496 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}On iter 303 the mean absolute change in the probability vector is : 0.00002 Average of the probability vector is: {txt}0.417 {res}First component regression {txt}(sum of wgt is 8.4826e+01) Linear regression Number of obs = {res} 203 {txt}F(28, 174) = {res} 887.84 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.8582 {txt}Root MSE = {res} .05108 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. Interval] {hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 11}q {c |}{col 14}{res}{space 2}-.0087915{col 26}{space 2} .0021906{col 37}{space 1} -4.01{col 46}{space 3}0.000{col 54}{space 4}-.0131151{col 67}{space 3}-.0044678 {txt}{space 9}cfs {c |}{col 14}{res}{space 2}-.0076239{col 26}{space 2} .0067937{col 37}{space 1} -1.12{col 46}{space 3}0.263{col 54}{space 4}-.0210326{col 67}{space 3} .0057847 {txt}{space 9}yr2 {c |}{col 14}{res}{space 2}-.1544999{col 26}{space 2} .023855{col 37}{space 1} -6.48{col 46}{space 3}0.000{col 54}{space 4}-.2015823{col 67}{space 3}-.1074175 {txt}{space 9}yr3 {c |}{col 14}{res}{space 2}-.1354367{col 26}{space 2} .026342{col 37}{space 1} -5.14{col 46}{space 3}0.000{col 54}{space 4}-.1874275{col 67}{space 3}-.0834458 {txt}{space 9}yr4 {c |}{col 14}{res}{space 2}-.1104961{col 26}{space 2} .0246569{col 37}{space 1} -4.48{col 46}{space 3}0.000{col 54}{space 4}-.1591613{col 67}{space 3} -.061831 {txt}{space 9}yr5 {c |}{col 14}{res}{space 2}-.1496658{col 26}{space 2} .0225323{col 37}{space 1} -6.64{col 46}{space 3}0.000{col 54}{space 4}-.1941376{col 67}{space 3} -.105194 {txt}{space 9}yr6 {c |}{col 14}{res}{space 2}-.0954302{col 26}{space 2} .0251456{col 37}{space 1} -3.80{col 46}{space 3}0.000{col 54}{space 4}-.1450599{col 67}{space 3}-.0458005 {txt}{space 9}yr7 {c |}{col 14}{res}{space 2} .0110278{col 26}{space 2} .0317068{col 37}{space 1} 0.35{col 46}{space 3}0.728{col 54}{space 4}-.0515516{col 67}{space 3} .0736072 {txt}{space 9}yr8 {c |}{col 14}{res}{space 2} .1203635{col 26}{space 2} .0799656{col 37}{space 1} 1.51{col 46}{space 3}0.134{col 54}{space 4} -.037464{col 67}{space 3} .278191 {txt}{space 9}yr9 {c |}{col 14}{res}{space 2}-.0320961{col 26}{space 2} .0314898{col 37}{space 1} -1.02{col 46}{space 3}0.309{col 54}{space 4}-.0942472{col 67}{space 3} .030055 {txt}{space 8}yr10 {c |}{col 14}{res}{space 2} -.08224{col 26}{space 2} .0271459{col 37}{space 1} -3.03{col 46}{space 3}0.003{col 54}{space 4}-.1358176{col 67}{space 3}-.0286623 {txt}{space 8}yr11 {c |}{col 14}{res}{space 2} -.099189{col 26}{space 2} .0183222{col 37}{space 1} -5.41{col 46}{space 3}0.000{col 54}{space 4}-.1353514{col 67}{space 3}-.0630265 {txt}{space 8}yr12 {c |}{col 14}{res}{space 2}-.0975293{col 26}{space 2} .0232266{col 37}{space 1} -4.20{col 46}{space 3}0.000{col 54}{space 4}-.1433714{col 67}{space 3}-.0516871 {txt}{space 8}yr13 {c |}{col 14}{res}{space 2} .0290844{col 26}{space 2} .0303094{col 37}{space 1} 0.96{col 46}{space 3}0.339{col 54}{space 4}-.0307371{col 67}{space 3} .0889059 {txt}{space 8}yr14 {c |}{col 14}{res}{space 2}-.1052829{col 26}{space 2} .0302728{col 37}{space 1} -3.48{col 46}{space 3}0.001{col 54}{space 4}-.1650321{col 67}{space 3}-.0455338 {txt}{space 8}yr15 {c |}{col 14}{res}{space 2}-.0097703{col 26}{space 2} .0328544{col 37}{space 1} -0.30{col 46}{space 3}0.767{col 54}{space 4}-.0746147{col 67}{space 3} .055074 {txt}{space 10}f2 {c |}{col 14}{res}{space 2}-.2263642{col 26}{space 2} .0250506{col 37}{space 1} -9.04{col 46}{space 3}0.000{col 54}{space 4}-.2758064{col 67}{space 3} -.176922 {txt}{space 10}f3 {c |}{col 14}{res}{space 2} -.10732{col 26}{space 2} .026328{col 37}{space 1} -4.08{col 46}{space 3}0.000{col 54}{space 4}-.1592835{col 67}{space 3}-.0553566 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.2533255{col 26}{space 2} .0293514{col 37}{space 1} -8.63{col 46}{space 3}0.000{col 54}{space 4}-.3112562{col 67}{space 3}-.1953948 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.2694819{col 26}{space 2} .0255593{col 37}{space 1} -10.54{col 46}{space 3}0.000{col 54}{space 4}-.3199281{col 67}{space 3}-.2190357 {txt}{space 10}f6 {c |}{col 14}{res}{space 2}-.2335836{col 26}{space 2} .0222729{col 37}{space 1} -10.49{col 46}{space 3}0.000{col 54}{space 4}-.2775435{col 67}{space 3}-.1896238 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.1690195{col 26}{space 2} .0315425{col 37}{space 1} -5.36{col 46}{space 3}0.000{col 54}{space 4}-.2312748{col 67}{space 3}-.1067643 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.3012887{col 26}{space 2} .0229273{col 37}{space 1} -13.14{col 46}{space 3}0.000{col 54}{space 4}-.3465402{col 67}{space 3}-.2560373 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.0490344{col 26}{space 2} .0241212{col 37}{space 1} -2.03{col 46}{space 3}0.044{col 54}{space 4}-.0966421{col 67}{space 3}-.0014267 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.0942914{col 26}{space 2} .0253001{col 37}{space 1} -3.73{col 46}{space 3}0.000{col 54}{space 4} -.144226{col 67}{space 3}-.0443567 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.2921631{col 26}{space 2} .0200009{col 37}{space 1} -14.61{col 46}{space 3}0.000{col 54}{space 4}-.3316387{col 67}{space 3}-.2526875 {txt}{space 9}f12 {c |}{col 14}{res}{space 2} -.128022{col 26}{space 2} .0250271{col 37}{space 1} -5.12{col 46}{space 3}0.000{col 54}{space 4}-.1774177{col 67}{space 3}-.0786263 {txt}{space 9}f13 {c |}{col 14}{res}{space 2} -.294806{col 26}{space 2} .020944{col 37}{space 1} -14.08{col 46}{space 3}0.000{col 54}{space 4} -.336143{col 67}{space 3} -.253469 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4448203{col 26}{space 2} .025041{col 37}{space 1} 17.76{col 46}{space 3}0.000{col 54}{space 4} .395397{col 67}{space 3} .4942435 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}Second component regression {txt}(sum of wgt is 1.1817e+02) Linear regression Number of obs = {res} 188 {txt}F(28, 159) = {res} 99489.82 {txt}Prob > F = {res} 0.0000 {txt}R-squared = {res} 0.7999 {txt}Root MSE = {res} .04207 {txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 14}{c |}{col 26} Robust {col 1} invrate{col 14}{c |} Coef.{col 26} Std. Err.{col 38} t{col 46} P>|t|{col 54} [95% Con{col 67}f. 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.0409286{col 37}{space 1} -2.22{col 46}{space 3}0.028{col 54}{space 4}-.1716119{col 67}{space 3}-.0099443 {txt}{space 10}f3 {c |}{col 14}{res}{space 2}-.1483131{col 26}{space 2} .0401642{col 37}{space 1} -3.69{col 46}{space 3}0.000{col 54}{space 4}-.2276373{col 67}{space 3} -.068989 {txt}{space 10}f4 {c |}{col 14}{res}{space 2}-.0993333{col 26}{space 2} .0407813{col 37}{space 1} -2.44{col 46}{space 3}0.016{col 54}{space 4}-.1798762{col 67}{space 3}-.0187905 {txt}{space 10}f5 {c |}{col 14}{res}{space 2}-.1642918{col 26}{space 2} .0414634{col 37}{space 1} -3.96{col 46}{space 3}0.000{col 54}{space 4}-.2461819{col 67}{space 3}-.0824017 {txt}{space 10}f6 {c |}{col 14}{res}{space 2} .2070152{col 26}{space 2} .0400909{col 37}{space 1} 5.16{col 46}{space 3}0.000{col 54}{space 4} .1278359{col 67}{space 3} .2861945 {txt}{space 10}f7 {c |}{col 14}{res}{space 2}-.0814641{col 26}{space 2} .0431107{col 37}{space 1} -1.89{col 46}{space 3}0.061{col 54}{space 4}-.1666076{col 67}{space 3} .0036795 {txt}{space 10}f8 {c |}{col 14}{res}{space 2}-.1241121{col 26}{space 2} .0407835{col 37}{space 1} -3.04{col 46}{space 3}0.003{col 54}{space 4}-.2046593{col 67}{space 3}-.0435648 {txt}{space 10}f9 {c |}{col 14}{res}{space 2}-.1002399{col 26}{space 2} .0417558{col 37}{space 1} -2.40{col 46}{space 3}0.018{col 54}{space 4}-.1827074{col 67}{space 3}-.0177723 {txt}{space 9}f10 {c |}{col 14}{res}{space 2}-.1399708{col 26}{space 2} .0475792{col 37}{space 1} -2.94{col 46}{space 3}0.004{col 54}{space 4}-.2339395{col 67}{space 3}-.0460021 {txt}{space 9}f11 {c |}{col 14}{res}{space 2}-.0260614{col 26}{space 2} .044322{col 37}{space 1} -0.59{col 46}{space 3}0.557{col 54}{space 4}-.1135972{col 67}{space 3} .0614744 {txt}{space 9}f12 {c |}{col 14}{res}{space 2}-.0485753{col 26}{space 2} .0440119{col 37}{space 1} -1.10{col 46}{space 3}0.271{col 54}{space 4}-.1354987{col 67}{space 3} .0383481 {txt}{space 9}f13 {c |}{col 14}{res}{space 2}-.0800105{col 26}{space 2} .0445908{col 37}{space 1} -1.79{col 46}{space 3}0.075{col 54}{space 4}-.1680773{col 67}{space 3} .0080562 {txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0839395{col 26}{space 2} .0406192{col 37}{space 1} 2.07{col 46}{space 3}0.040{col 54}{space 4} .0037168{col 67}{space 3} .1641622 {txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{txt}(206 real changes made) {res}This Switching Regression took {com}13{res} seconds. {txt}(0 real changes made) {com}. local fcswitch1 pohhpf pohkzf pohwwf invhpf invkzf invwwf {txt} {com}. foreach k of local fcswitch1 {c -(} {txt} 2{com}. tab fcba `k', row nofreq all exact {txt} 3{com}. {c )-} {txt}{c |} pohhpf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 6.58 93.42 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 30.32 69.68 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 22.51 77.49 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 16.4814 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 19.2994 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} -0.2671 {txt} gamma = {res} -0.7214 {txt}ASE = {res}0.119 {txt} Kendall's tau-b = {res} -0.2671 {txt}ASE = {res}0.048 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} pohkzf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 7.89 92.11 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 32.26 67.74 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 24.24 75.76 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 16.4818 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 18.9737 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} -0.2671 {txt} gamma = {res} -0.6949 {txt}ASE = {res}0.119 {txt} Kendall's tau-b = {res} -0.2671 {txt}ASE = {res}0.050 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} pohwwf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 9.21 90.79 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 28.39 71.61 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 22.08 77.92 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 10.9008 {txt} Pr = {res}0.001 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 12.2269 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} -0.2172 {txt} gamma = {res} -0.5924 {txt}ASE = {res}0.141 {txt} Kendall's tau-b = {res} -0.2172 {txt}ASE = {res}0.053 {txt} Fisher's exact = {res}0.001 {txt} 1-sided Fisher's exact = {res}0.001 {txt}{c |} invhpf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 32.89 67.11 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 54.84 45.16 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 47.62 52.38 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 9.8449 {txt} Pr = {res}0.002 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 10.0068 {txt} Pr = {res}0.002 {txt} Cram{c e'}r's V = {res} -0.2064 {txt} gamma = {res} -0.4248 {txt}ASE = {res}0.120 {txt} Kendall's tau-b = {res} -0.2064 {txt}ASE = {res}0.063 {txt} Fisher's exact = {res}0.002 {txt} 1-sided Fisher's exact = {res}0.001 {txt}{c |} invkzf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 21.05 78.95 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 63.87 36.13 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 49.78 50.22 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 37.3991 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 39.2137 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} -0.4024 {txt} gamma = {res} -0.7379 {txt}ASE = {res}0.075 {txt} Kendall's tau-b = {res} -0.4024 {txt}ASE = {res}0.058 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} invwwf fcba {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 30.26 69.74 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 58.71 41.29 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 49.35 50.65 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 16.5092 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 16.8589 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} -0.2673 {txt} gamma = {res} -0.5323 {txt}ASE = {res}0.107 {txt} Kendall's tau-b = {res} -0.2673 {txt}ASE = {res}0.062 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt} {com}. gen byte diba1=diba>0.5 {txt} {com}. local fcswitch2 pohhpd pohkzd pohwwd invhpd invkzd invwwd {txt} {com}. foreach k of local fcswitch2 {c -(} {txt} 2{com}. tab diba1 `k', row nofreq all exact {txt} 3{com}. {c )-} {txt}{c |} pohhpd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 49.18 50.82 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 27.06 72.94 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 32.90 67.10 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 9.9512 {txt} Pr = {res}0.002 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 9.6095 {txt} Pr = {res}0.002 {txt} Cram{c e'}r's V = {res} 0.2076 {txt} gamma = {res} 0.4458 {txt}ASE = {res}0.124 {txt} Kendall's tau-b = {res} 0.2076 {txt}ASE = {res}0.068 {txt} Fisher's exact = {res}0.002 {txt} 1-sided Fisher's exact = {res}0.002 {txt}{c |} pohkzd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 100.00 0.00 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 0.00 100.00 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 26.41 73.59 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res}231.0000 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res}266.6989 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} 1.0000 {txt} gamma = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Kendall's tau-b = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} pohwwd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 100.00 0.00 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 0.00 100.00 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 26.41 73.59 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res}231.0000 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res}266.6989 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} 1.0000 {txt} gamma = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Kendall's tau-b = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} invhpd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 49.18 50.82 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 27.06 72.94 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 32.90 67.10 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res} 9.9512 {txt} Pr = {res}0.002 {txt} likelihood-ratio chi2({res}1{txt}) = {res} 9.6095 {txt} Pr = {res}0.002 {txt} Cram{c e'}r's V = {res} 0.2076 {txt} gamma = {res} 0.4458 {txt}ASE = {res}0.124 {txt} Kendall's tau-b = {res} 0.2076 {txt}ASE = {res}0.068 {txt} Fisher's exact = {res}0.002 {txt} 1-sided Fisher's exact = {res}0.002 {txt}{c |} invkzd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 100.00 0.00 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 0.00 100.00 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 26.41 73.59 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res}231.0000 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res}266.6989 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} 1.0000 {txt} gamma = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Kendall's tau-b = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt}{c |} invwwd diba1 {c |} 0 1 {c |} Total {hline 11}{c +}{hline 22}{c +}{hline 10} 0 {c |}{res} 100.00 0.00 {txt}{c |}{res} 100.00 {txt} 1 {c |}{res} 0.00 100.00 {txt}{c |}{res} 100.00 {txt}{hline 11}{c +}{hline 22}{c +}{hline 10} Total {c |}{res} 26.41 73.59 {txt}{c |}{res} 100.00 {txt} Pearson chi2({res}1{txt}) = {res}231.0000 {txt} Pr = {res}0.000 {txt} likelihood-ratio chi2({res}1{txt}) = {res}266.6989 {txt} Pr = {res}0.000 {txt} Cram{c e'}r's V = {res} 1.0000 {txt} gamma = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Kendall's tau-b = {res} 1.0000 {txt}ASE = {res}0.000 {txt} Fisher's exact = {res}0.000 {txt} 1-sided Fisher's exact = {res}0.000 {txt} {com}. log close {txt}name: {res} {txt}log: {res}C:\Thesis Research - Final\Thesis\Chapter2\Data_analysis\switch.smcl {txt}log type: {res}smcl {txt}closed on: {res} 7 Sep 2017, 09:56:20 {txt}{.-} {smcl} {txt}{sf}{ul off}