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Bias Correction Of Semiparametric Long Memory Parameter Estimators Via The Prefiltered Sieve Bootstrap

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

Abstract

This paper investigates bootstrap-based bias correction of semiparametric estimators of the long memory parameter, d, in fractionally integrated processes. The re-sampling 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

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  • Poskitt, D. S. & Martin, Gael M. & Grose, Simone D., 2017. "Bias Correction Of Semiparametric Long Memory Parameter Estimators Via The Prefiltered Sieve Bootstrap," Econometric Theory, Cambridge University Press, vol. 33(3), pages 578-609, June.
    • Handle: RePEc:cup:etheor:v:33:y:2017:i:03:p:578-609_00
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    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    2. Li, Jia & Phillips, Peter C. B. & Shi, Shuping & Yu, Jun, 2022. "Weak Identification of Long Memory with Implications for Inference," Economics and Statistics Working Papers 8-2022, Singapore Management University, School of Economics.
    3. Zhanshou Chen & Yanting Xiao & Fuxiao Li, 2021. "Monitoring memory parameter change-points in long-memory time series," Empirical Economics, Springer, vol. 60(5), pages 2365-2389, May.

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