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Short‐term forecasting of GDP using large datasets: a pseudo real‐time forecast evaluation exercise
Author(s) -
Rünstler G.,
Barhoumi K.,
Benk S.,
Cristadoro R.,
Den Reijer A.,
Jakaitiene A.,
Jelonek P.,
Rua A.,
Ruth K.,
Van Nieuwenhuyze C.
Publication year - 2009
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1105
Subject(s) - exploit , computer science , term (time) , econometrics , context (archaeology) , dynamic factor , series (stratigraphy) , economics , paleontology , physics , computer security , quantum mechanics , biology
This paper performs a large‐scale forecast evaluation exercise to assess the performance of different models for the short‐term forecasting of GDP, resorting to large datasets from ten European countries. Several versions of factor models are considered and cross‐country evidence is provided. 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 factor models perform best and models that exploit monthly information outperform models that use purely quarterly data. However, the improvement over the simpler, quarterly models remains contained. Copyright © 2009 John Wiley & Sons, Ltd.