Are disaggregate data useful for factor analysis in forecasting French GDP?
Article first published online: 1 DEC 2009
Copyright © 2009 John Wiley & Sons, Ltd.
Journal of Forecasting
Special Issue: Advances in Business Cycle Analysis and Forecasting
Volume 29, Issue 1-2, pages 132–144, January - March 2010
How to Cite
Barhoumi, K., Darné, O. and Ferrara, L. (2010), Are disaggregate data useful for factor analysis in forecasting French GDP?. J. Forecast., 29: 132–144. doi: 10.1002/for.1162
- Issue published online: 19 FEB 2010
- Article first published online: 1 DEC 2009
- GDP forecasting;
- factor models;
- data aggregation
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.