A large Bayesian vector autoregression model for Russia
Deryugina, Elena; Ponomarenko, Alexey (04.12.2014)
JulkaisusarjaBOFIT Discussion Papers
JulkaisijaBank of Finland
Julkaisun pysyvä osoite onhttps://urn.fi/URN:NBN:fi:bof-201412193432
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
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.