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

Karim Barhoumi (), Olivier Darné and Laurent Ferrara ()

Documents de Travail from Banque de 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. 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.

Keywords: GDP forecasting; Factor models; Data aggregation. (search for similar items in EconPapers)
JEL-codes: C13 C52 C53 F47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
Date: Written
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Persistent link: http://EconPapers.repec.org/RePEc:bfr:banfra:232

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