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Prévoir sans persistance

  • Christophe Boucher
  • Bertrand Maillet

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. Classification JEL�: C14, C53, G17.

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Article provided by Presses de Sciences-Po in its journal Revue économique.

Volume (Year): 63 (2012)
Issue (Month): 3 ()
Pages: 581-590

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Handle: RePEc:cai:recosp:reco_633_0581
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  1. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
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  8. Michael Jansson & Marcelo J. Moreira, 2004. "Optimal Inference in Regression Models with Nearly Integrated Regressors," NBER Technical Working Papers 0303, National Bureau of Economic Research, Inc.
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  11. Christophe Boucher & Bertrand Maillet, 2011. "Une analyse temps-fréquences des cycles financiers," Revue économique, Presses de Sciences-Po, vol. 62(3), pages 441-450.
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