Accounting for Post-Crisis Macroeconomic Developments in Russia: A Large Bayesian Vector Autoregression Model Approach

Abstract

ABSTRACTWe apply an econometric approach developed specifically to address the “curse of dimensionality” in Russian data and estimate a Bayesian vector autoregression model comprising sixteen major macroeconomic indicators. We conduct several types of exercises to validate our model: impulse response analysis, recursive forecasting and counterfactual simulations. We also show that real sector developments in Russia in 2010–13 could be accurately forecasted if conditioned on oil price and EU GDP (but not if conditioned on oil price alone). Real growth rates were notably lower than projected in 2014, presumably due to increased economic uncertainty.