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Predictive Regressions: A Reduced-Bias Estimation Method

  • Amihud, Yakov
  • Hurvich, Clifford M.

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 that 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 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.

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Article provided by Cambridge University Press in its journal Journal of Financial and Quantitative Analysis.

Volume (Year): 39 (2004)
Issue (Month): 04 (December)
Pages: 813-841

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Handle: RePEc:cup:jfinqa:v:39:y:2004:i:04:p:813-841_00
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  1. Malcolm Baker & Jeremy C. Stein, 2002. "Market Liquidity as a Sentiment Indicator," Harvard Institute of Economic Research Working Papers 1977, Harvard - Institute of Economic Research.
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  7. Gregory Mankiw, N. & Shapiro, Matthew D., 1986. "Do we reject too often? : Small sample properties of tests of rational expectations models," Economics Letters, Elsevier, vol. 20(2), pages 139-145.
  8. Robert F. Stambaugh, 1999. "Predictive Regressions," NBER Technical Working Papers 0240, National Bureau of Economic Research, Inc.
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  15. Lewellen, Jonathan, 2003. "Predicting Returns With Financial Ratios," Working papers 4374-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
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