Bias correction of semiparametric long memory parameter estimators via the prefiltered sieve bootstrap

D. S. Poskitt, Gael M. Martin, Simone D. Grose

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, d, in fractionally integrated processes. The resampling method involves the application of the sieve bootstrap to data prefiltered by a preliminary semiparametric estimate of the long memory parameter. Theoretical justification for using the bootstrap technique to bias adjust log periodogram and semiparametric local Whittle estimators of the memory parameter is provided in the case where the true value of d lies in the range 0 ≤ d < 0.5. That the bootstrap method provides confidence intervals with the correct asymptotic coverage is also proven, with the intervals shown to adjust explicitly for bias, as estimated via the bootstrap. Simulation evidence comparing the performance of the bootstrap bias correction with analytical bias-correction techniques is presented. The bootstrap method is shown to produce notable bias reductions, in particular when applied to an estimator for which some degree of bias reduction has already been accomplished by analytical means.
Original languageEnglish
Pages (from-to)578-609
Number of pages32
JournalEconometric Theory
Volume33
Issue number3
DOIs
Publication statusPublished - Jun 2017

Keywords

  • Bias adjustment
  • bootstrap-based inference
  • fractional process
  • log-periodogram regressio
  • local Whittle estimator

Cite this

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title = "Bias correction of semiparametric long memory parameter estimators via the prefiltered sieve bootstrap",
abstract = "This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, d, in fractionally integrated processes. The resampling method involves the application of the sieve bootstrap to data prefiltered by a preliminary semiparametric estimate of the long memory parameter. Theoretical justification for using the bootstrap technique to bias adjust log periodogram and semiparametric local Whittle estimators of the memory parameter is provided in the case where the true value of d lies in the range 0 ≤ d < 0.5. That the bootstrap method provides confidence intervals with the correct asymptotic coverage is also proven, with the intervals shown to adjust explicitly for bias, as estimated via the bootstrap. Simulation evidence comparing the performance of the bootstrap bias correction with analytical bias-correction techniques is presented. The bootstrap method is shown to produce notable bias reductions, in particular when applied to an estimator for which some degree of bias reduction has already been accomplished by analytical means.",
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Bias correction of semiparametric long memory parameter estimators via the prefiltered sieve bootstrap. / Poskitt, D. S.; Martin, Gael M.; Grose, Simone D.

In: Econometric Theory, Vol. 33, No. 3, 06.2017, p. 578-609.

Research output: Contribution to journalArticleResearchpeer-review

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