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Short-Term Forecasting of GDP under Structural Changes

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  • Rafael Ravnik

    (The Croatian National Bank, Croatia)

Abstract

This paper proposes several models with time-varying parameters, estimated by Bayesian techniques used for the shortterm forecasting of Croatian GDP. In addition to domestic variables, the models include EU GDP, so that the specificities of a small open economy have been taken into account. The predictive ability of the models is compared with the naive benchmark forecast. The results indicate that the modelling of time-varying parameters improves GDP forecasts in comparison with the naive benchmark model, and in addition, it has been established that mean forecast errors for all tested models with time-varying parameters are smaller than the errors of equally specified fixed parameter models.

Suggested Citation

  • Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    • Handle: RePEc:hnb:wpaper:40
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
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    4. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    5. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    6. Davor Kunovac & Borna Špalat, 2014. "Nowcasting GDP Using Available Monthly Indicators," Working Papers 39, The Croatian National Bank, Croatia.
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    11. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
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    Cited by:

    1. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    2. Rafael Ravnik & Nikola Bokan, 2018. "Quarterly Projection Model for Croatia," Surveys 34, The Croatian National Bank, Croatia.

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    More about this item

    Keywords

    GDP forecasts; Bayesian models with time-varying parameters;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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