Template-Type: ReDIF-Paper 1.0
Author-Name: Konstantin Styrin
Author-Email: styrinka@cbr.ru
Author-Workplace-Name: Bank of Russia, Russian Federation


Title: Forecasting inflation in Russia by Dynamic Model Averaging

Abstract: In this study, I forecast CPI inflation in Russia by the method of Dynamic Model Averaging
(Raftery et al., 2010; Koop and Korobilis, 2012) pseudo out-of-sample on historical data. This
method can be viewed as an extension of the Bayesian Model Averaging where the identity of a
model that generates data and model parameters are allowed to change over time. The DMA is
shown not to produce forecasts superior to simpler benchmarks even if a subset of individual
predictors is pre-selected “with the benefit of hindsight” on the full sample. The two groups of
predictors that feature the highest average values of the posterior inclusion probability are loans to
non-financial firms and individuals along with actual and anticipated wages.


Length: 44 pages
Creation-Date: 2018-12
Revision-Date:
Publication-Status:
File-URL: http://cbr.ru/Content/Document/File/87593/wp39_e.pdf
File-Format: Application/pdf
File-Function:
Number:wps39
Classification-JEL: C5, C53, E37. 
Keywords: Bayesian model averaging, model uncertainty, econometric modeling, high-dimension model, inflation forecast. 
Handle:RePEc:bkr:wpaper:wps39