IDEAS home Printed from https://ideas.repec.org/p/dnb/dnbwpp/320.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.dnb.nl/en/binaries/Working%20Paper%20320_tcm47-261110.pdf
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:ecmode:v:69:y:2018:i:c:p:160-168 is not listed on IDEAS
    2. repec:eee:intfor:v:33:y:2017:i:4:p:786-800 is not listed on IDEAS
    3. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Working Paper – WP/16/01- Nowcasting Real GDP growth in South Africa," Papers 7068, South African Reserve Bank.
    4. 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.
    5. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 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.
    6. 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.
    7. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    8. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.
    9. 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.
    10. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2016. "A bottom-up approach for forecasting GDP in a data rich environment," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    11. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    12. 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.
    13. 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dnb:dnbwpp:320. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rob Vet). General contact details of provider: http://edirc.repec.org/data/dnbgvnl.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.