A large Bayesian vector autoregression model for Russia

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Title: A large Bayesian vector autoregression model for Russia
Author: Deryugina, Elena ; Ponomarenko, Alexey
Organization: Bank of Finland
Department / Unit: Institute for Economies in Transition (BOFIT)
Series: BOFIT Discussion Papers
Series number: 22/2014
Year of publication: 2014
Publication date: 4.12.2014
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.
Pages: 24
Subject (yso): ekonometriset mallit; makrotaloustiede
Keywords: Bofit-kokoelma; Venäjä; Russia
JEL: E32; E44; E47; C32
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
Rights: https://helda.helsinki.fi/bof/copyright


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