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Prévoir sans persistance

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  • Christophe Boucher

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet

    ()
    (A.A.Advisors-QCG - ABN AMRO, LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans, EIF - Europlace Institute of Finance)

Abstract

La littérature sur la prévision économique et financière a identifié trois problèmes importants : l'instabilité des régressions prédictives, la discordance des résultats des tests de prévisions en échantillon et hors échantillon, et la difficile inférence statistique lorsque les prédicteurs sont hautement persistants. Dans cet article, nous abordons ces trois questions simultanément, en proposant de traiter en amont la persistance des variables prédictives. Nous retirons ainsi préalablement les composantes basses fréquences des prédicteurs et nous montrons, en simulations et sur des données financières, que ce pré-traitement permet d'améliorer leur pouvoir prédictif.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00662771.

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Date of creation: Jan 2012
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Handle: RePEc:hal:cesptp:halshs-00662771

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Keywords: Prévision; persistance; filtres.;

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  1. John Y. Campbell & Motohiro Yogo, 2003. "Efficient Tests of Stock Return Predictability," NBER Working Papers 10026, National Bureau of Economic Research, Inc.
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