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Prévoir sans persistance

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  • Christophe Boucher

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet

    (A.A.Advisors-QCG - ABN AMRO, EIF - Europlace Institute of Finance, LEO - Laboratoire d'Économie d'Orleans [UMR7322] - UO - Université d'Orléans - UT - Université de Tours - CNRS - Centre National de la Recherche Scientifique)

Abstract

The forecasting literature has identified three important and broad issues: the predictive content is unstable over time, in-sample and out-of-sample discordant results and the problematic statistical inference with highly persistent predictors. In this paper, we simultaneously address these three issues, proposing to directly treat the persistence of forecasting variables before use. We thus cut-out the low frequency components and show, in simulations and on financial data, that this pre-treatment improves the predictive power of the studied economic variables.

Suggested Citation

  • Christophe Boucher & Bertrand Maillet, 2012. "Prévoir sans persistance," Working Papers halshs-00662771, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00662771
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00662771v1
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