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A BVAR Model for Forecasting of Czech Inflation

Author

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  • Frantisek Brazdik
  • Michal Franta

Abstract

Bayesian vector autoregressions (BVAR) have turned out to be useful for medium-term macroeconomic forecasting. Several features of the Czech economy strengthen the rationale for using this approach. These include in particular the short time series available and uncertainty about long-run trends. We compare forecasts based on a small-scale mean-adjusted BVAR with the official forecasts published by the Czech National Bank (CNB) over the period 2008q3-2016q4. The comparison demonstrates that the BVAR approach can provide more precise inflation forecasts over the monetary policy horizon. For other macroeconomic variables, the CNB forecasts either outperform or are comparable with the forecasts based on the BVAR model.

Suggested Citation

  • Frantisek Brazdik & Michal Franta, 2017. "A BVAR Model for Forecasting of Czech Inflation," Working Papers 2017/7, Czech National Bank.
    • Handle: RePEc:cnb:wpaper:2017/7
    as

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    File URL: https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2017_07.pdf
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    References listed on IDEAS

    as
    1. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    2. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    3. repec:cnb:ocpubv:rb13/1 is not listed on IDEAS
    4. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    5. Michal Andrle & Tibor Hledik & Ondra Kamenik & Jan Vlcek, 2009. "Implementing the New Structural Model of the Czech National Bank," Working Papers 2009/2, Czech National Bank.
    6. Chris Bloor, 2009. "The use of statistical forecasting models at the Reserve Bank of New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 72, pages 21-26, June.
    7. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
    8. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
    2. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation," IHEID Working Papers 05-2021, Economics Section, The Graduate Institute of International Studies.
    3. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    4. Michal Franta & Tomas Holub & Branislav Saxa, 2018. "Balance Sheet Implications of the Czech National Bank's Exchange Rate Commitment," Working Papers 2018/10, Czech National Bank.

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    More about this item

    Keywords

    BVAR; forecast evaluation; inflation targeting; real-time forecasting;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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