Detrending Persistent Predictors
AbstractResearchers 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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Bibliographic InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00587775.
Date of creation: Mar 2011
Date of revision:
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Forecasting; persistence; detrending; expected returns.;
Other versions of this item:
- Christophe Boucher & Bertrand Maillet, 2011. "Detrending Persistent Predictors," Documents de travail du Centre d'Economie de la Sorbonne 11019, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- G1 - Financial Economics - - General Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-04-30 (All new papers)
- NEP-ETS-2011-04-30 (Econometric Time Series)
- NEP-FOR-2011-04-30 (Forecasting)
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