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Forecasting inflation in Russia by Dynamic Model Averaging

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  • Konstantin Styrin

    (Bank of Russia, Russian Federation)

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.

Suggested Citation

  • Konstantin Styrin, 2018. "Forecasting inflation in Russia by Dynamic Model Averaging," Bank of Russia Working Paper Series wps39, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps39
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian model averaging; model uncertainty; econometric modeling; high-dimension model; inflation forecast.;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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