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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 Economies in Transition

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

JEL-codes: E32 E44 E47 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cis, nep-for and nep-mac
Date: 2014-12-04
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Published in Published in Emerging Markets Finance and Trade, vol. 51(6), pages 1261 – 1275, October 2015 as Accounting for Post-Crisis Macroeconomic Developments in Russia: A Large Bayesian Vector Autoregression Model Approach.

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