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Validity Of The Sampling Window Method For Long-Range Dependent Linear Processes


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  • Nordman, Daniel J.
  • Lahiri, Soumendra N.
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    the sampling window method of hall, jing, and lahiri (1998, statistica sinica 8, 1189 1204) is known to consistently estimate the distribution of the sample mean for a class of long-range dependent processes, generated by transformations of gaussian time series. this paper shows that the same nonparametric subsampling method is also valid for an entirely different category of long-range dependent series that are linear with possibly non-gaussian innovations. for these strongly dependent time processes, subsampling confidence intervals allow inference on the process mean without knowledge of the underlying innovation distribution or the long-memory parameter. the finite-sample coverage accuracy of the subsampling method is examined through a numerical study.the authors thank two referees for comments and suggestions that greatly improved an earlier draft of the paper. this research was partially supported by u.s. national science foundation grants dms 00-72571 and dms 03-06574 and by the deutsche forschungsgemeinschaft (sfb 475).

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    Bibliographic Info

    Article provided by Cambridge University Press in its journal Econometric Theory.

    Volume (Year): 21 (2005)
    Issue (Month): 06 (December)
    Pages: 1087-1111

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    Handle: RePEc:cup:etheor:v:21:y:2005:i:06:p:1087-1111_05

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    Cited by:
    1. Ting Zhang & Hwai-Chung Ho & Martin Wendler & Wei Biao Wu, 2013. "Block Sampling under Strong Dependence," Papers 1312.5807,
    2. Daniel J. Nordman & Philipp Sibbertsen & Soumendra N. Lahiri, 2007. "Empirical likelihood confidence intervals for the mean of a long-range dependent process," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 576-599, 07.
    3. Zhang, Ting & Ho, Hwai-Chung & Wendler, Martin & Wu, Wei Biao, 2013. "Block sampling under strong dependence," Stochastic Processes and their Applications, Elsevier, vol. 123(6), pages 2323-2339.


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