Predictive Regressions: A Reduced-Bias Estimation Method

64 Pages Posted: 3 Nov 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: November 2003

Abstract

We propose a direct and convenient reduced-bias estimator of predictive regression coefficients, assuming that the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. For the single regressorsmodel, Stambaugh (1999) shows that the ordinary least squares estimator of the predictive regression coefficient is biased in small samples. Our estimation method employs an augmented regression which uses a proxy for the errors in the autoregressive model. We also develop a heuristic estimator of the standard error of the estimated predictive coefficient which performs well in simulations. We analyze the case of multiple predictors that are first-order autoregressive and derive bias expressions for both the ordinary least squares and our reduced-bias estimated coefficients. The effectiveness of our estimation method is demonstrated by simulations.

Keywords: Stock Returns, Dividend Yields, Autoregressive Models

Suggested Citation

Amihud, Yakov and Hurvich, Clifford M., Predictive Regressions: A Reduced-Bias Estimation Method (November 2003). NYU Working Paper No. FIN-02-019. Available at SSRN: https://ssrn.com/abstract=1294197

Yakov Amihud (Contact Author)

New York University - Stern School of Business ( email )

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