Working Paper Series no. 215: Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise.

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.

Karim Barhoumi, Gerhard Rünstler, Riccardo Cristadoro, Ard Den Reijer, Audrone Jakaitiene, Piotr Jelonek, Antonio Rua, Karsten Ruth, Szilard Benk and Christophe Van Nieuwenhuyze
July 2008

Classification JEL : E37, C53.

Keywords : Bridge models, Dynamic factor models, real-time data flow.