Jackknife for bias reduction in predictive regressions

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

View at publisher


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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

37 since deposited on 09 Oct 2012
9 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 54040
Item Type: Journal Article
Refereed: Yes
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: 09 Oct 2012 23:50
Last Modified: 09 Nov 2014 05:59

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page