BOFIT Discussion Papers, Institute for Economies in Transition, Bank of Finland
No 22/2014:
A large Bayesian vector autoregression model for Russia
Elena Deryugina ()
and Alexey Ponomarenko ()
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).
Keywords: Bayesian vector autoregression; forecasting; Russia; (follow links to similar papers)
JEL-Codes: C32; E32; E44; E47; (follow links to similar papers)
24 pages, December 3, 2014
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