Are disaggregate data useful for factor analysis in forecasting French GDP?
AbstractThis 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. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with three disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice.
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Bibliographic InfoPaper provided by Banque de France in its series Working papers with number 232.
Length: 27 pages
Date of creation: 2009
Date of revision:
GDP forecasting ; Factor models ; Data aggregation.;
Other versions of this item:
- 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.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-07-17 (All new papers)
- NEP-ECM-2009-07-17 (Econometrics)
- NEP-FOR-2009-07-17 (Forecasting)
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