文章基本信息

标题：A large Bayesian vector autoregression model for Russia
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作者：Elena Deryugina ; Alexey Ponomarenko
期刊名称：BOFIT Discussion Papers
印刷版ISSN：1456-4564
电子版ISSN：1456-5889
出版年度：2014
出版社：Bank of Finland Institute for Economies in Transition (BOFIT)
摘要：We apply an econometric approach developed specifically to address the ‘curse of dimen-sionality’ 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 re-sponse analysis, recursive forecasting and counter factual simulation. Our results demon-strate 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 accurate-ly forecast if conditioned on the oil price and EU GDP (but not if conditioned on the oil price alone).
关键词：Bayesian vector autoregression; forecasting; Russia