Optimal jackknife for unit root models
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 se...
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2015
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sg-smu-ink.soe_research-28662020-04-01T08:25:15Z Optimal jackknife for unit root models CHEN, Ye Jun YU, 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. 2015-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1866 info:doi/10.1016/j.spl.2014.12.014 https://ink.library.smu.edu.sg/context/soe_research/article/2866/viewcontent/7271199.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bias reduction Variance reduction Jackknife Autoregression Econometrics |
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Bias reduction Variance reduction Jackknife Autoregression Econometrics |
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Bias reduction Variance reduction Jackknife Autoregression Econometrics CHEN, Ye Jun YU, Optimal jackknife for unit root models |
| description |
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. |
| format |
text |
| author |
CHEN, Ye Jun YU, |
| author_facet |
CHEN, Ye Jun YU, |
| author_sort |
CHEN, Ye |
| title |
Optimal jackknife for unit root models |
| title_short |
Optimal jackknife for unit root models |
| title_full |
Optimal jackknife for unit root models |
| title_fullStr |
Optimal jackknife for unit root models |
| title_full_unstemmed |
Optimal jackknife for unit root models |
| title_sort |
optimal jackknife for unit root models |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2015 |
| url |
https://ink.library.smu.edu.sg/soe_research/1866 https://ink.library.smu.edu.sg/context/soe_research/article/2866/viewcontent/7271199.pdf |
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