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

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G. Rünstler, Karim Barhoumi (), Szilárd Benk (), R. Cristadoro, A. Den Reijer, Audrone Jakaitiene (), P. Jelonek, A. Rua, K. Ruth and C. Van Nieuwenhuyze
Additional contact information
G. Rünstler: European Central Bank, Frankfurt|Main, Germany, Postal: European Central Bank, Frankfurt|Main, Germany
R. Cristadoro: Banca d'Italia, Rome, Italy, Postal: Banca d'Italia, Rome, Italy
A. Den Reijer: Sveriges Riksbank, Stockholm, Sweden, Postal: Sveriges Riksbank, Stockholm, Sweden
P. Jelonek: Narodowy Bank Polski, Warsaw, Poland, Postal: Narodowy Bank Polski, Warsaw, Poland
A. Rua: Banco de Portugal, Lisbon, Portugal, Postal: Banco de Portugal, Lisbon, Portugal
K. Ruth: Deutsche Bundesbank, Frankfurt, Germany, Postal: Deutsche Bundesbank, Frankfurt, Germany
C. Van Nieuwenhuyze: National Bank of Belgium, Brussels, Belgium, Postal: National Bank of Belgium, Brussels, Belgium

Journal of Forecasting, 2009, vol. 28, issue 7, pages 595-611

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.

Date: 2009

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Persistent link: http://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:7:p:595-611

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