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Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise

Author

Listed:
  • G. Rünstler

    (European Central Bank, Frankfurt|Main, Germany)

  • K. Barhoumi

    (Banque de France, Paris, France)

  • S. Benk

    (Magyar Nemzeti Bank, Budapest, Hungary)

  • R. Cristadoro

    (Banca d'Italia, Rome, Italy)

  • A. Den Reijer

    (Sveriges Riksbank, Stockholm, Sweden)

  • A. Jakaitiene

    (Institute of Mathematics and Informatics, Vilnius, Lithuania)

  • P. Jelonek

    (Narodowy Bank Polski, Warsaw, Poland)

  • A. Rua

    (Banco de Portugal, Lisbon, Portugal)

  • K. Ruth

    (Deutsche Bundesbank, Frankfurt, Germany)

  • C. Van Nieuwenhuyze

    (National Bank of Belgium, Brussels, Belgium)

Abstract

This paper performs a large-scale forecast evaluation exercise to assess the performance of different models for the short-term forecasting of GDP, resorting to large datasets from ten European countries. Several versions of factor models are considered and cross-country evidence is provided. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that factor models perform best and models that exploit monthly information outperform models that use purely quarterly data. However, the improvement over the simpler, quarterly models remains contained. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
  • Handle: RePEc:jof:jforec:v:28:y:2009:i:7:p:595-611
    DOI: 10.1002/for.1105
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    References listed on IDEAS

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