A large Bayesian vector autoregression model for Russia

Elena Deryugina and Alexey Ponomarenko

No 22/2014, BOFIT Discussion Papers from Bank of Finland Institute for Emerging Economies (BOFIT)

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 macroeconomic indicators as well as external sector variables. We conduct several types of exercise to validate our model: impulse response analysis, recursive forecasting and counter factual simulation. Our results demonstrate that the employed 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). Publication

Keywords: Bayesian vector autoregression; forecasting; Russia (search for similar items in EconPapers)
JEL-codes: C32 E32 E44 E47 (search for similar items in EconPapers)
Date: 2014
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Working Paper: A large Bayesian vector autoregression model for Russia (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bofitp:bdp2014_022

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