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Forecasting Inflation in Russia Using Dynamic Model Averaging

Author

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  • Konstantin Styrin

    (New Economic School, Bank of Russia)

Abstract

In this study, I forecast CPI inflation in Russia using the method of Dynamic Model Averaging pseudo out-of-sample on historical data. This method can be viewed as an extension of Bayesian Model Averaging, where the identity of the model that generates data is allowed to change over time, as are the model parameters. 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’ from the full sample. The two groups of predictors that give the highest average values for the posterior inclusion probability are loans to non-financial firms and individuals, along with actual and anticipated wages.

Suggested Citation

  • Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
    • Handle: RePEc:bkr:journl:v:78:y:2019:i:1:p:3-18
    DOI: 10.31477/rjmf.201901.03
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    References listed on IDEAS

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    Cited by:

    1. Doojav Gan-Ochir & Luvsannyam Davaajargal, 2023. "Forecasting Inflation in Mongolia: A Dynamic Model Averaging Approach," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 27-48, January.
    2. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    3. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    4. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    5. Viacheslav Kramkov, 2023. "Does CPI disaggregation improve inflation forecast accuracy?," Bank of Russia Working Paper Series wps112, Bank of Russia.

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    More about this item

    Keywords

    Bayesian model averaging; model uncertainty; econometric modelling; 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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