Detrending Persistent Predictors.

Researchers in finance very often rely on highly persistent Ñ nearly integrated Ñ explanatory variables to predict returns. This paper proposes to stand up to the usual problem of persistent regressor bias, by detrending the highly auto-correlated predictors. We find that the statistical evidence of out-of-sample predictability of stock returns is stronger, once predictors are adjusted for high persistence.

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Year of publication:	2011-03
Authors:	Boucher, Christophe ; Maillet, Bertrand
Institutions:	Centre d'Économie de la Sorbonne, Université Paris 1 (Panthéon-Sorbonne)
Subject:	Forecasting \| persistence \| detrending \| expected returns

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Extent:	application/pdf
Series:	Documents de travail du Centre d'Economie de la Sorbonne. - ISSN 1955-611X.
Type of publication:	Book / Working Paper
Language:	English
Notes:	10 pages
Classification:	C1 - Econometric and Statistical Methods: General ; C14 - Semiparametric and Nonparametric Methods ; G1 - General Financial Markets
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009003411