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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26463
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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: | 8-Nov-2003 |
| Series/Report no.: | FIN-02-019 |
| 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 regressors model,
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. |
| URI: | http://hdl.handle.net/2451/26463 |
| Appears in Collections: | Finance Working Papers
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