Short-Term Forecasting of GDP

[Sapphire Capital]

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

Karim Barhoumi
affiliation not provided to SSRN

Szilard Benk
Central European University - Department of Economics

Riccardo Cristadoro
Bank of Italy

Ard Den Reijer
Bank of the Netherlands - Research Department

Audrone Jakaitiene
Vytautas Magnus University

Piotr Jelonek
European University Institute

Antonio Rua
Bank of Portugal - Economic Research Department

Gerhard Rünstler
European Central Bank

Karsten Ruth
J. W. Goethe University Frankfurt

Christophe Van Nieuwenhuyze
National Bank of Belgium


April 2008

ECB Occasional Paper No. 84


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.

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