A large Bayesian vector autoregression model for Russia

We apply an econometric approach developed specifically to address the "curse of dimensionality" in Russian data and estimate a Bayesian vector autoregression model comprising 14 major domestic real, price and monetary macroeco-nomic indicators as well as external sector variables. We conduct several types of exercise to validate our model: im-pulse response analysis, recursive forecasting and counter factual simulation. Our results demonstrate that the em-ployed methodology is highly appropriate for economic modelling in Russia. We also show that post-crisis real sector developments in Russia could be accurately forecast if conditioned on the oil price and EU GDP (but not if conditioned on the oil price alone).