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

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  • Van Nieuwenhuyze, Christophe
  • Benk, Szilard
  • Rünstler, Gerhard
  • Cristadoro, Riccardo
  • Den Reijer, Ard
  • Jakaitiene, Audrone
  • Jelonek, Piotr
  • Rua, António
  • Ruth, Karsten
  • Barhoumi, Karim

Abstract

This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. 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 models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. JEL Classification: E37, C53

Suggested Citation

  • Van Nieuwenhuyze, Christophe & Benk, Szilard & Rünstler, Gerhard & Cristadoro, Riccardo & Den Reijer, Ard & Jakaitiene, Audrone & Jelonek, Piotr & Rua, António & Ruth, Karsten & Barhoumi, Karim, 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
    • Handle: RePEc:ecb:ecbops:200884
    Note: 407979
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    Keywords

    bridge models; dynamic factor models; real-time data flow;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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