Forecasting Oil Prices: Can Large BVARs Help?

Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the
forecasting literature and most of this work has focused on macroeconomic variables. In this
paper, we examine the ability of large BVARs to forecast the real price of crude oil using a
large dataset with over 100 variables. We find consistent results that the large BVARs do not
beat the BVARs with small and medium sizes for short forecast horizons but offer better
forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find
that the forecast ability of the large models further improves upon the competing standard
BVARs once endowed with flexible error structures.