Forecasting Russian Macroeconomic Indicators with BVAR

This paper evaluates the forecast performance of Bayesian vector autoregressions (BVARs) on Russian data. We estimate BVARs of different sizes and compare the accuracy of their out-of-sample forecasts with those obtained with unrestricted vector autoregressions and random walk with drift. We show that many Russian macroeconomic indicators can be forecast by BVARs more accurately than by competing models. However, contrary to several other studies, we do not confirm that the relative forecast error monotonically decreases with increasing the cross-sectional dimension of the sample. In half of those cases where a BVAR appears to be the most accurate model, a small-dimensional BVAR outperforms its high-dimensional counterpart