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Prévoir sans persistance

  • Christophe Boucher

    ()

    (Axe Finance - CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet

    ()

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

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.

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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
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00662771
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  1. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, 05.
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  9. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
  10. Christophe Boucher & Bertrand Maillet, 2011. "Une analyse temps-fréquences des cycles financiers," Revue économique, Presses de Sciences-Po, vol. 62(3), pages 441-450.
  11. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
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