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Short-term forecasting of French GDP growth using dynamic factor models

Author

Listed:
  • Marie Bessec

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris-Dauphine)

  • Catherine Doz

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In recent years, central banks and international organisations have been making ever greater use of factor models to forecast macroeconomic variables. We examine the performance of these models in forecasting French GDP growth over short horizons. The factors are extracted from a large data set of around one hundred variables including survey balances and real, financial, and international variables. An out-of-sample pseudo real-time evaluation over the past decade shows that factor models provide a gain in accuracy relative to the usual benchmarks. However, the forecasts remain inaccurate before the start of the quarter. We also show that the inclusion of international and financial variables can improve forecasts at the longest horizons.

Suggested Citation

  • Marie Bessec & Catherine Doz, 2014. "Short-term forecasting of French GDP growth using dynamic factor models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01515602, HAL.
  • Handle: RePEc:hal:cesptp:hal-01515602 DOI: 10.1787/jbcma-2013-5jz742l0pt8s Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01515602
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    1. Manuel F. Bagues & Mauro Sylos Labini, 2009. "Do Online Labor Market Intermediaries Matter? The Impact of AlmaLaurea on the University-to-Work Transition," NBER Chapters,in: Studies of Labor Market Intermediation, pages 127-154 National Bureau of Economic Research, Inc.
    2. John Knowles & Nicola Persico & Petra Todd, 2001. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 203-232, February.
    3. Jennifer L. Doleac & Luke C.D. Stein, 2013. "The Visible Hand: Race and Online Market Outcomes," Economic Journal, Royal Economic Society, vol. 123(11), pages 469-492, November.
    4. Michael Ewens & Bryan Tomlin & Liang Choon Wang, 2014. "Statistical Discrimination or Prejudice? A Large Sample Field Experiment," The Review of Economics and Statistics, MIT Press, pages 119-134.
    5. Marianne Bertrand & Sendhil Mullainathan, 2004. "Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination," American Economic Review, American Economic Association, pages 991-1013.
    6. Agrawal, Ajay & Lacetera, Nicola & Lyons, Elizabeth, 2016. "Does standardized information in online markets disproportionately benefit job applicants from less developed countries?," Journal of International Economics, Elsevier, vol. 103(C), pages 1-12.
    7. John A. List, 2004. "The Nature and Extent of Discrimination in the Marketplace: Evidence from the Field," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 49-89.
    8. David H. Autor & David Scarborough, 2008. "Does Job Testing Harm Minority Workers? Evidence from Retail Establishments," The Quarterly Journal of Economics, Oxford University Press, vol. 123(1), pages 219-277.
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    Cited by:

    1. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.

    More about this item

    Keywords

    GDP forecast; factor models;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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