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Are disaggregate data useful for factor analysis in forecasting French GDP?

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  • Karim Barhoumi

    (Banque de France, DGEI-DCPM, Paris, France)

  • Olivier Darné
  • Laurent Ferrara

    (Banque de France, DGEI-DCPM, Paris, France)

Abstract

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.

Suggested Citation

  • Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
  • Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:132-144
    DOI: 10.1002/for.1162
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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