Jackknife Bias Reduction in the Presence of a Near-Unit Root

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generatin...

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Main Authors: Marcus J. Chambers, Maria Kyriacou
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Econometrics
Subjects:
Online Access:http://www.mdpi.com/2225-1146/6/1/11
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author Marcus J. Chambers
Maria Kyriacou
author_facet Marcus J. Chambers
Maria Kyriacou
author_sort Marcus J. Chambers
collection DOAJ
description This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings.
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spelling doaj.art-62aa7e9ad89f47afbee357d0d1a89d092022-12-22T03:09:56ZengMDPI AGEconometrics2225-11462018-03-01611110.3390/econometrics6010011econometrics6010011Jackknife Bias Reduction in the Presence of a Near-Unit RootMarcus J. Chambers0Maria Kyriacou1Department of Economics, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UKDepartment of Economics, University of Southampton, Southampton SO17 1BJ, UKThis paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings.http://www.mdpi.com/2225-1146/6/1/11Jackknifebias reductionnear-unit rootmoment generating function
spellingShingle Marcus J. Chambers
Maria Kyriacou
Jackknife Bias Reduction in the Presence of a Near-Unit Root
Econometrics
Jackknife
bias reduction
near-unit root
moment generating function
title Jackknife Bias Reduction in the Presence of a Near-Unit Root
title_full Jackknife Bias Reduction in the Presence of a Near-Unit Root
title_fullStr Jackknife Bias Reduction in the Presence of a Near-Unit Root
title_full_unstemmed Jackknife Bias Reduction in the Presence of a Near-Unit Root
title_short Jackknife Bias Reduction in the Presence of a Near-Unit Root
title_sort jackknife bias reduction in the presence of a near unit root
topic Jackknife
bias reduction
near-unit root
moment generating function
url http://www.mdpi.com/2225-1146/6/1/11
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