Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models

This paper evaluates the performance of 11 vector autoregressive models in forecasting 15 macroeconomic variables for the Indian economy over the 2007:01 to 2011:10 out-of-sample period. We consider 3 classical VARs, 4 Bayesian VARs and 4 Bayesian Factor Augmented VARs. Comparing the performance by minimum average RMSEs of the models to the benchmark random walk model, we find that in general, the 11 models outperform the random walk model. Although, there is no specific model that outperforms others at all horizons for any of the variables, the Bayesian VARs and Bayesian Factor Augmented VAR models on average outperform the classical VARs. We also provide an ex ante forecast using the selected `best' models and find that these models do not perfectly capture the turning points in each of the series pointing to the importance of conducting future research in a non-linear framework.