Comparison of Vector Autoregressive Model (VAR) And Bayesian Vector Autoregressive Model (BVAR) Models for Modelling Economic Growth

The study attempted to develop forecasting model for inflation as well as IPP growth in a multivariate time series Bayesian framework, known as Bayesian Vector Autoregressive (BVAR) model. The main advantage of using this model is the incorporation of prior information which may boost the forecasting performance of the model. The results revealed that the diagnostics results of the models are appear to be satisfactory and out of sample percentage root mean square error (PRMSE) for WPI for four quarters is 1.4932 percent, whereas, for IIP, it is 4.2508 percent. Further, for selecting the a suitable values for lambda and theta, we have tried various combination for these parameters between 0 to 1 and based on PRMSE, we found that lambda=0.3 and theta=0.9 are suitable values for BVAR(2). Therefore, BVAR(2) with lambda=0.3 and theta=0.9 was fitted. From the results, it can be observed that, out of sample PRMSE has been reduced while using BVAR in both the cases i.e. for WPI as well as IIP.Based on the comparison of forecasting performance of VAR and BVAR model, measured in terms of out-of-sample percentage root mean square error, it was found that BVAR model performed better than VAR model in case of inflation as well as IPP growth forecas