Bayesian Approach for Indonesia Inflation Forecasting

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Abstract

This paper presents a Bayesian approach to find the Bayesian model for the point forecast of ARMA model under normal-gamma prior assumption with quadratic loss function in the form of mathematical expression. The conditional posterior predictive density is obtained from the combination of the posterior under normal-gamma prior with the conditional predictive density. The marginal conditional posterior predictive density is obtained by integrating the conditional posterior predictive density, whereas the point forecast is derived from the marginal conditional posterior predictive density. Furthermore, the forecasting model is applied to inflation data and compare to traditional method. The results show that the Bayesian forecasting is better than the traditional forecasting.

Keywords:  ARMA model, Bayes theorem, Inflation,  Normal-gamma prior

JEL Classifications: C13, C15, C22

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Author Biography

Zul - Amry, State University of Medan

Department of Mathematics

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Published

2018-09-04

How to Cite

Amry, Z. .-. (2018). Bayesian Approach for Indonesia Inflation Forecasting. International Journal of Economics and Financial Issues, 8(5), 96–102. Retrieved from https://www.econjournals.com/index.php/ijefi/article/view/6870

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