A large Bayesian vector autoregression model for Russia

Elena Deryugina and Alexey Ponomarenko

No wps1, Bank of Russia Working Paper Series from Bank of Russia

Abstract: 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).

Keywords: Bayesian vector autoregression; forecasting; Russia (search for similar items in EconPapers)
JEL-codes: C32 E32 E44 E47 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2015-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Related works:
Working Paper: A large Bayesian vector autoregression model for Russia (2014)
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