Comparative Evaluation of Forecast Accuracies for ARIMA, Exponential Smoothing and VAR

Authors

  • Hatice Erkekoglu Erciyes University
  • Aweng Peter Majok Garang Erciyes University
  • Adire Simon Deng

Abstract

While various linear and nonlinear forecasting models exist, multivariate methods like VAR, Exponential smoothing, and Box-Jenkins' ARIMA methodology constitute the widely used methods in time series.  This paper employs series of Turkish private consumption, exports and GDP data ranging between 1998: Q1 and 2017: Q4 to analyze the forecast performance of the three models using measures of accuracy such as RMSE, MAE, MAPE, Theil's  & . Seasonal decomposition and ADF unit root tests were performed to obtain new deseasonalized series and stationarity, respectively. Results offer preference for the use of ARIMA in forecasting, having performed better than VAR and exponential smoothing in all scenarios. Additionally, VAR model provided better forecast accuracy than exponential smoothing on all measures of accuracy except on Thiel's  whose VAR values were not computed. Cautionary use of ARIMA for forecasting is recommended.

Keywords: Forecast Evaluation, ARIMA, Exponential Smoothing, VAR

JEL Classifications: C1, E00, C51

DOI: https://doi.org/10.32479/ijefi.9020

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Author Biographies

Hatice Erkekoglu, Erciyes University

Prof. Dr.Erciyes Üniversitesi, Uygulamalı Bilimler Yüksekokulu, Uluslarası Ticaret ve Lojistik Bölümü, 38039 Kayseri

Aweng Peter Majok Garang, Erciyes University

Erciyes University, Ph.D. Economics program, Faculty of Economic and Administrative Sciences 38039 Kayseri, TR

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Published

2020-11-20

How to Cite

Erkekoglu, H., Garang, A. P. M., & Deng, A. S. (2020). Comparative Evaluation of Forecast Accuracies for ARIMA, Exponential Smoothing and VAR. International Journal of Economics and Financial Issues, 10(6), 206–216. Retrieved from https://www.econjournals.com/index.php/ijefi/article/view/9020

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