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Are disaggregate data useful for factor analysis in forecasting French GDP?
Author(s) -
Barhoumi Karim,
Darné Olivier,
Ferrara Laurent
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.1162
Subject(s) - nowcasting , dynamic factor , econometrics , aggregate (composite) , computer science , factor analysis , principal component analysis , time series , series (stratigraphy) , technology forecasting , economics , machine learning , artificial intelligence , meteorology , geography , paleontology , materials science , composite material , biology
This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components obtained using time and frequency domain methods. We question whether it is more appropriate to use aggregate or disaggregate data to extract the factors used in forecasting equations. The forecasting accuracy is evaluated for various forecast horizons considering both rolling and recursive schemes. We empirically show that static factors, estimated from a small database, lead to competitive results, especially for nowcasting. Copyright © 2009 John Wiley & Sons, Ltd.