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Forecasting GDP growth in times of crisis: private sector forecasts versus statistical models

Author

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  • Jasper de Winter

Abstract

This paper examines the accuracy of short run forecasts of Dutch GDP growth by several linear statistical models and private sector analysts. We focus on the financial crisis of 2008-2009 and the dot-com recession of 2001-2002. The dynamic factor model turns out to be the best model. Its forecast accuracy during the crisis deteriorates much less than that of the other linear models and hardly at all when backcasting and nowcasting. Moreover, the dynamic factor model beats the private sector forecasters at nowcasting. This finding suggests that adding judgement to a mechanical model may not improve short-term forecasting performance.

Suggested Citation

  • Jasper de Winter, 2011. "Forecasting GDP growth in times of crisis: private sector forecasts versus statistical models," DNB Working Papers 320, Netherlands Central Bank, Research Department.
    • Handle: RePEc:dnb:dnbwpp:320
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    File URL: https://www.dnb.nl/en/binaries/Working%20Paper%20320_tcm47-261110.pdf
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    Citations

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    Cited by:

    1. Francisco Dias & Maximiano Pinheiro & António Rua, 2018. "A bottom-up approach for forecasting GDP in a data-rich environment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 718-723, June.
    2. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    3. repec:eee:ecmode:v:69:y:2018:i:c:p:160-168 is not listed on IDEAS
    4. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    5. repec:eee:intfor:v:33:y:2017:i:4:p:786-800 is not listed on IDEAS
    6. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Nowcasting Real GDP growth in South Africa," Working Papers 7068, South African Reserve Bank.
    7. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    8. António Rua & Francisco Craveiro Dias & Maximiano Pinheiro, 2014. "Forecasting Portuguese GDP with factor models," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    9. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    10. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    11. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.
    12. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

    More about this item

    Keywords

    Nowcasting; Professional Forecasters; Factor Model; Forecasting;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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