A note on stationary bootstrap variance estimator under long-range dependence

Taegyu Kang, Young Min Kim, Jongho Im

Research output: Contribution to journalArticlepeer-review

Abstract

The stationary bootstrap method is popularly used to compute the standard errors or confidence regions of estimators, generated from time processes exhibiting weakly dependent stationarity. Most previous stationary bootstrap methods have focused on studying large-sample properties of stationary bootstrap inference about a sample mean under short-range dependence. For long-range dependence, recent studies have investigated the properties of block bootstrap methods using overlapping and non-overlapping blocking techniques with fixed block lengths. However, the characteristics of a stationary bootstrap with random block lengths are less well-known under long-range dependence. We investigate the asymptotic property of a stationary bootstrap variance estimator for a sample mean under long-range dependence. Our theoretical and simulation results indicate that the stationary bootstrap method does not have n−consistency for stationary and long-range dependent time processes.

Original languageEnglish
Article number108971
JournalStatistics and Probability Letters
Volume169
DOIs
Publication statusPublished - 2021 Feb

Bibliographical note

Funding Information:
We thank the Editor, the Associate Editor and an anonymous reviewer for the constructive comments that have much improved the quality of this manuscript. The second author’s work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20204010600060 ), and the corresponding author’s work was supported by National Research Foundation (NRF) Korea ( NRF-2018R1D1A1B07045220 ).

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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