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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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    1. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    2. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    3. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    4. Onorante, Luca & Raftery, Adrian E., 2016. "Dynamic model averaging in large model spaces using dynamic Occam׳s window," European Economic Review, Elsevier, vol. 81(C), pages 2-14.
    5. Arina Sapova & Aleksey Porshakov & Andrey Andreev & Evgenia Shatilo, 2018. "Review of Methodological Specifics of Consumer Price Index Seasonal Adjustment in the Bank of Russia," Bank of Russia Working Paper Series wps33, Bank of Russia.
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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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