The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study

This paper analyzes the mechanics of VAR forecast pooling and quantifies the forecast performance under varying conditions. To fill the gap between empirical and purely theoretical research we run a Monte Carlo study and simulate the data from different New Keynesian DSGE models. We find that equally pooling VAR forecasts outperforms single predictions in general and that the gains are substantial for sample sizes relevant in practice. In contrast, the estimation of theoretically optimal weights or model selection is advisable only for very large data sets hardly available in practice. Notably, equally pooling forecasts from small-scale VARs can even dominate forecasts from large VARs including all relevant variables. Given our results, we advocate the use of equally pooled predictions from parsimonious VARs as an easy to implement and competitive forecast approach.