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A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets

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  • Liang Guo-Fitoussi

    (RITM - Réseaux Innovation Territoires et Mondialisation - RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11)

  • Olivier Darné

    () (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes)

Abstract

In this paper, we compare the properties of the main criteria proposed for selecting the number of factors in dynamic factor model in a small sample. Both static and dynamic factor numbers' selection rules are studied. Simulations show that the GR ratio proposed by Ahn and Horenstein (2013) and the criterion proposed by Onatski (2010) outperform the others. Furthermore, the two criteria can select accurately the number of static factors in a dynamic factors design. Also, the criteria proposed by Hallin and Liska (2007) and Breitung and Pigorsch (2009) correctly select the number of dynamic factors in most cases. However, empirical applications show most criteria select only one factor in presence of one strong factor.

Suggested Citation

  • Liang Guo-Fitoussi & Olivier Darné, 2014. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," Working Papers hal-00962247, HAL.
  • Handle: RePEc:hal:wpaper:hal-00962247
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00962247
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    Cited by:

    1. Francesco Trebbi & Eric Weese, 2015. "Insurgency and Small Wars: Estimation of Unobserved Coalition Structures," NBER Working Papers 21202, National Bureau of Economic Research, Inc.
    2. Mao Takongmo, Charles Olivier & Stevanovic, Dalibor, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233, Mars-Juin.
    3. repec:kob:wpaper:1628 is not listed on IDEAS

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    Keywords

    Dynamic factor model; factor numbers; small sample properties;

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