Bias correction of persistence measures in fractionally integrated models

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This article investigates the accuracy of bootstrap-based bias correction of persistence measures for long-memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long-memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data prefiltered by a semi-parametric estimate of the long-memory parameter. Both versions of the bootstrap technique are used to estimate the finite-sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available. The basic sieve technique is seen to yield a reduction in the bias of both persistence measures. The prefiltered sieve produces a substantial further reduction in the bias of the estimated impulse response function, whilst the extra improvement yielded by prefiltering in the case of the sample autocorrelation function is shown to depend heavily on the accuracy of the prefilter.
Original languageEnglish
Pages (from-to)721 - 740
Number of pages20
JournalJournal of Time Series Analysis
Volume36
Issue number5
DOIs
Publication statusPublished - 2015

Cite this

@article{9e3720b8c93144ef9230cdd4bb924c54,
title = "Bias correction of persistence measures in fractionally integrated models",
abstract = "This article investigates the accuracy of bootstrap-based bias correction of persistence measures for long-memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long-memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data prefiltered by a semi-parametric estimate of the long-memory parameter. Both versions of the bootstrap technique are used to estimate the finite-sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available. The basic sieve technique is seen to yield a reduction in the bias of both persistence measures. The prefiltered sieve produces a substantial further reduction in the bias of the estimated impulse response function, whilst the extra improvement yielded by prefiltering in the case of the sample autocorrelation function is shown to depend heavily on the accuracy of the prefilter.",
author = "Grose, {Simone Deborah} and Martin, {Gael Margaret} and Poskitt, {Don Stephen}",
year = "2015",
doi = "10.1111/jtsa.12116",
language = "English",
volume = "36",
pages = "721 -- 740",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "5",

}

Bias correction of persistence measures in fractionally integrated models. / Grose, Simone Deborah; Martin, Gael Margaret; Poskitt, Don Stephen.

In: Journal of Time Series Analysis, Vol. 36, No. 5, 2015, p. 721 - 740.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Bias correction of persistence measures in fractionally integrated models

AU - Grose, Simone Deborah

AU - Martin, Gael Margaret

AU - Poskitt, Don Stephen

PY - 2015

Y1 - 2015

N2 - This article investigates the accuracy of bootstrap-based bias correction of persistence measures for long-memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long-memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data prefiltered by a semi-parametric estimate of the long-memory parameter. Both versions of the bootstrap technique are used to estimate the finite-sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available. The basic sieve technique is seen to yield a reduction in the bias of both persistence measures. The prefiltered sieve produces a substantial further reduction in the bias of the estimated impulse response function, whilst the extra improvement yielded by prefiltering in the case of the sample autocorrelation function is shown to depend heavily on the accuracy of the prefilter.

AB - This article investigates the accuracy of bootstrap-based bias correction of persistence measures for long-memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long-memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data prefiltered by a semi-parametric estimate of the long-memory parameter. Both versions of the bootstrap technique are used to estimate the finite-sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available. The basic sieve technique is seen to yield a reduction in the bias of both persistence measures. The prefiltered sieve produces a substantial further reduction in the bias of the estimated impulse response function, whilst the extra improvement yielded by prefiltering in the case of the sample autocorrelation function is shown to depend heavily on the accuracy of the prefilter.

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DO - 10.1111/jtsa.12116

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JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

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