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A BVAR Model for Forecasting Ukrainian Inflation and GDP

Author

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  • Nadiia Shapovalenko

    (National Bank of Ukraine)

Abstract

In this paper, I examine the forecasting performance of a Bayesian Vector Autoregression (BVAR) model with a steady-state prior and compare the accuracy of the forecasts against the QPM and official NBU forecasts during the Q1 2016–Q1 2020 period. My findings suggest that inflation forecasts produced by the BVAR model are more accurate than those of the QPM model for two quarters ahead and are competitive for a longer time horizon. The BVAR forecasts for GDP growth also outperform those of the QPM but for the whole forecast horizon. Moreover, it is revealed that the BVAR model demonstrates a better performance compared to the NBU’s official inflation forecasts over the monetary policy horizon, whereas the opposite is true for GDP growth forecasts. Future research may deal with estimation issues brought about by COVID-19.

Suggested Citation

  • Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation and GDP," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 251, pages 14-36.
    • Handle: RePEc:ukb:journl:y:2021:i:251:p:14-36
    DOI: 10.26531/vnbu2021.251.02
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    File URL: https://journal.bank.gov.ua/en/article/2021/251/02
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    References listed on IDEAS

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    1. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
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    3. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    4. Beechey, Meredith & Österholm, Pär, 2010. "Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors," International Journal of Forecasting, Elsevier, vol. 26(2), pages 248-264, April.
    5. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    forecasting; inflation; GDP; BVAR; forecast evaluation;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • 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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