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Prévoir sans persistance

Christophe Boucher () and Bertrand Maillet
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Christophe Boucher: CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Date: 2012-05
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00820714
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Published in Revue Economique, Presses de Sciences Po, 2012, 63 (3), pp.581-590. <10.3917/reco.633.0581>

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