Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose

Karim Barhoumi, Olivier Darné () and Laurent Ferrara
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Karim Barhoumi: International Monetary Fund (IMF)
Olivier Darné: LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes

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Abstract: GDP forecasts based on dynamic factor models, applied to a large data set, are now widely used by practitioners involved in nowcasting and short‐term macroeconomic forecasting. One recurrent empirical question that arises when dealing with such models is the way to determine the optimal number of factors. At the same time, statistical tests have recently been put forward in the literature in order to optimally determine the number of significant factors. In this article, we propose to reconcile both fields of interest by selecting the number of factors, through a testing procedure, to include in the forecasting equation. Through an empirical exercise on French and German GDPs, we assess the impact of a battery of recent statistical tests for the number of factors for a forecasting purpose. By implementing a rolling experience, we also assess the stability of the results overtime.

Date: 2012-12-21
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Published in Oxford Bulletin of Economics and Statistics, 2012, 75 (1), pp.64-79. ⟨10.1111/obes.12010⟩

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Related works:
Journal Article: Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose (2013)
Working Paper: Testing the number of factors: An empirical assessment for forecasting purposes (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04344628

DOI: 10.1111/obes.12010

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