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

Karim Barhoumi (), Szilárd Benk (), Riccardo Cristadoro (), Ard Den Reijer (), Audrone Jakaitiene (), Piotr Jelonek, António Rua, Gerhard Rünstler (), Karsten Ruth and Christophe Van Nieuwenhuyze

No 84, Occasional Paper Series from European Central Bank

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.

Keywords: Bridge models; Dynamic factor models; real-time data flow. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-eec and nep-for
Date: 2008-04
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Related works:
Working Paper: Short-Term Forecasting of GDP Using Large Monthly Datasets: A Presudo Real-Time Forecast Evaluation Exercise (2008)
Working Paper: Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise (2008)
Working Paper: Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise (2008)
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