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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26317
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| Title: | Predictive Regressions: A Reduced-Bias Estimation Method |
| Authors: | Amihud, Yakov
Hurvich, Clifford M. |
| Keywords: | Stock Returns
Dividend Yields
Autoregressive Models |
| Issue Date: | 4-May-2004 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-2004-1 |
| 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. |
| URI: | http://hdl.handle.net/2451/26317 |
| Appears in Collections: | IOMS: Statistics Working Papers
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