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Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise

Gerhard Rünstler (), Karim Barhoumi (), Szilard Benk (), Riccardo Cristadoro (), Ard Reijer (), Audrone Jakaitiene (), P. Jelonek, António Rua, K. Ruth and C. Van Nieuwenhuyze

No 1, Bank of Lithuania Working Paper Series from Bank of Lithuania

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.

Keywords: Bridge models; Dynamic factor models; real-time data flow model (search for similar items in EconPapers)
JEL-codes: E37 C53 (search for similar items in EconPapers)
Date: 2008-09-01
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Related works:
Working Paper: Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise (2008) Downloads
Working Paper: Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise (2008) Downloads
Working Paper: Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise (2008) Downloads
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