Detrending Persistent Predictors

Researchers in finance very often rely on highly persistent - nearly integrated - 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:	HAL
Subject:	Forecasting \| persistence \| detrending \| expected returns

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Extent:	application/pdf
Series:	Post-Print.
Type of publication:	Book / Working Paper
Notes:	View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00587775
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010605314