Improving the finite sample performance of tests for a shift in mean

Daisuke Yamazaki, Eiji Kurozumi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

It is widely known that structural break tests based on the long-run variance estimator, which is estimated under the alternative, suffer from serious size distortion when the errors are serially correlated. In this paper, we propose bias-corrected tests for a shift in mean by correcting the bias of the long-run variance estimator up to O(1/. T). Simulation results show that the proposed tests have good size and high power.

Original languageEnglish
Pages (from-to)144-173
Number of pages30
JournalJournal of Statistical Planning and Inference
Volume167
DOIs
Publication statusPublished - Dec 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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