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Jackknife for bias reduction in predictive regressions

Zhu, Min (2012) Jackknife for bias reduction in predictive regressions. Journal of Financial Econometrics.

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Abstract

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.

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ID Code: 54040
Item Type: Journal Article
Keywords: return predictability, Predictive regressions
DOI: 10.1093/jjfinec/nbs011
ISSN: 1479-8417
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2012 The Author.
Copyright Statement: Published by Oxford University Press. All rights reserved.
Deposited On: 10 Oct 2012 09:50
Last Modified: 09 Nov 2014 15:59

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