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.
|Date of creation:||Mar 2011|
|Date of revision:|
|Publication status:||Published in Documents de travail du Centre d'Economie de la Sorbonne 2011.19 - ISSN : 1955-611X. 2011|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00587775|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
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