Comparison of Forecasting Performance with VAR vs. ARIMA Models Using Economic Variables of Bangladesh
Md. Salauddin Khan, Umama Khan
Asian Journal of Probability and Statistics · pp. 33–47 · Published 14 Dec 2020
10.9734/ajpas/2020/v10i230243Abstract
The main concept of this research was forecasting a group of variables simultaneously, thus making use of correlations among the variables. This research aims to check forecasting performance among different VAR and ARIMA models applying some economic indicators of Bangladesh. Data sets were collected from secondary sources of Bangladesh such as Bangladesh bank bulletin, Bangladesh economic review, Monthly economic trends of Bangladesh Bank, and Statistical yearbook of Bangladesh. The stationary VAR and ARIMA models were applied for predicting these financial variables and then checked the accuracy by comparing ME, RMSE, MAE, MPE, MAPE, and MASE of respected the variables. This research found that the VAR model presented a better forecast than ARIMA models for the highly correlated variables such as GDP vs. GNP, Export vs. Import, etc. But ARIMA and VAR models performed almost the same for comparatively low correlated variables. That's means the variables were comparatively low correlated couldn't give a better forecast in the multivariate time series model rather than the univariate time series model. Finally, researchers concluded that before forecasting the authority should check correlations among the variables, and for high correlated variables, the VAR model should be used for forecasting, and otherwise, they can consider any models for both of these correlated and uncorrelated variables.
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