Predictive Regressions: A Reduced-Bias Estimation Method

65 Pages Posted: 31 Oct 2008

See all articles by Yakov Amihud

Yakov Amihud

New York University - Stern School of Business

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

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Date Written: May 2004

Abstract

Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable; see Stambaugh (1999) for the single-regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients we develop a heuristic estimator which performs well in simulations, for both the single-predictor model and an important specification of the multiple-predictor model. The effectiveness of our method is demonstrated by simulations and by empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.

Keywords: Stock Returns, Dividend Yields, Autoregressive Models

Suggested Citation

Amihud, Yakov and Hurvich, Clifford M., Predictive Regressions: A Reduced-Bias Estimation Method (May 2004). Statistics Working Papers Series, Vol. , pp. -, 2004. Available at SSRN: https://ssrn.com/abstract=1290978

Yakov Amihud (Contact Author)

New York University - Stern School of Business ( email )

44 West 4th Street
Suite 9-190
New York, NY 10012-1126
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212-998-0720 (Phone)
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Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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