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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. 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.
    2. 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.
    3. Davor Kunovac & Borna Špalat, 2014. "Nowcasting GDP Using Available Monthly Indicators," Working Papers 39, The Croatian National Bank, Croatia.
    4. 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.
    5. Davor Kunovac, 2013. "The Borrowing Costs of Selected Countries of the European Union – the Role of the Spillover of External Shocks," Working Papers 38, The Croatian National Bank, Croatia.
    6. Kim, Chang-Jin & Nelson, Charles R, 2001. "A Bayesian Approach to Testing for Markov-Switching in Univariate and Dynamic Factor Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 989-1013, November.
    7. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    8. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    9. Ivo Krznar & Davor Kunovac, 2010. "Impact of External Shocks on Domestic Inflation and GDP," Working Papers 26, The Croatian National Bank, Croatia.
    10. Ana Maria Čeh & Mirna Dumičić & Ivo Krznar, 2011. "A Credit Market Disequilibrium Model And Periods of Credit Crunch," Working Papers 28, The Croatian National Bank, Croatia.
    11. 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.).
    12. Davor Kunovac, 2011. "Asymmetric correlations on the Croatian equity market," Financial Theory and Practice, Institute of Public Finance, vol. 35(1), pages 1-24.
    13. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
    14. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
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    Cited by:

    1. Rafael Ravnik & Nikola Bokan, 2018. "Quarterly Projection Model for Croatia," Surveys 34, The Croatian National Bank, Croatia.
    2. 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.

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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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