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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26345
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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: | 26-Nov-2002 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-2002-7 |
| 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-regressor 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, and show that the estimated
coefficient of the errors and its squared standard error are unbiased.
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/2451/26345 |
| Appears in Collections: | IOMS: Statistics Working Papers
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