Frequency domain bootstrap for ratio statistics under long-range dependence

Young Min Kim, Jongho Im

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A frequency domain bootstrap (FDB) is a common technique to apply Efron's independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.

Original languageEnglish
Pages (from-to)547-560
Number of pages14
JournalJournal of the Korean Statistical Society
Issue number4
Publication statusPublished - 2019 Dec

Bibliographical note

Funding Information:
The authors would like to thank the editor, associate editor and reviewers for their careful readings and thoughtful comments. The authors are supported by National Research Foundation of Korea (NRF), NRF-2016R1D1A1B03932212 & NRF-2018R1D1A1B07045220, respectively.

Publisher Copyright:
© 2019 The Korean Statistical Society

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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