Publication Type
Journal Article
Version
submittedVersion
Publication Date
4-2015
Abstract
A new jackknife method is introduced to remove the first order bias in unit root models. It is optimal in the sense that it minimizes the variance among all the jackknife estimators of the form considered in Phillips and Yu (2005) and Chambers and Kyriacou (2013) after the number of subsamples is selected. Simulations show that the new jackknife reduces the variance of that of Chambers and Kyriacou by about 10% for any selected number of subsamples without compromising bias reduction. The results continue to hold true in near unit root models. (C) 2014 Elsevier B.V. All rights reserved.
Keywords
Bias reduction, Variance reduction, Jackknife, Autoregression
Discipline
Econometrics
Research Areas
Econometrics
Publication
Statistics and Probability Letters
Volume
99
First Page
135
Last Page
142
ISSN
0167-7152
Identifier
10.1016/j.spl.2014.12.014
Publisher
Elsevier
Citation
CHEN, Ye and Jun YU.
Optimal jackknife for unit root models. (2015). Statistics and Probability Letters. 99, 135-142.
Available at: https://ink.library.smu.edu.sg/soe_research/1866
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.spl.2014.12.014