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Прогнозирование Основных Российских Макроэкономических Показателей С Помощью Tvp-Модели С Байесовским Сжатием Параметров
[Forecasting key Russian macroeconomic variables using a TVP model with Bayesian shrinkage]

Author

Listed:
  • Polbin, Andrey
  • Shumilov, Andrei

Abstract

The paper examines the quality of forecasts of Russian GDP and its components (household consumption, investment, exports and imports) using a model with Bayesian shrinkage of time-varying parameters (TVP) based on hierarchical normal-gamma prior. Such models account for the possible nonlinearity of relationships and, at the same time, can deal with the overfitting problem. We find that, compared to simpler benchmarks, the Bayesian TVP model with exogenous predictors gives better forecasts for GDP at horizons of 2-4 quarters, and for investment – at horizons of 1-3 quarters. When predicting other components of GDP, Bayesian TVP models do not demonstrate systematic superiority over other models.

Suggested Citation

  • Polbin, Andrey & Shumilov, Andrei, 2024. "Прогнозирование Основных Российских Макроэкономических Показателей С Помощью Tvp-Модели С Байесовским Сжатием Параметров [Forecasting key Russian macroeconomic variables using a TVP model with Baye," MPRA Paper 120170, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:120170
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    File URL: https://mpra.ub.uni-muenchen.de/120170/1/MPRA_paper_120170.pdf
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    References listed on IDEAS

    as
    1. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    2. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    3. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
    4. Polbin, Andrey & Shumilov, Andrei, 2023. "Прогнозирование Инфляции В России С Помощью Tvp-Модели С Байесовским Сжатием Параметров [Forecasting inflation in Russia using a TVP model with Bayesian shrinkage]," MPRA Paper 118650, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    forecasting; Russian GDP and its components; time-varying parameter model; Bayesian shrinkage; normal-gamma prior;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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