First-order bias correction for fractionally integrated time series

Jaechoul Lee, Kyungduk Ko

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non-negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag-one sample autocorrelation-based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag-one sample autocorrelation up to the order n-1. An example where the exact first-order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data.

Original languageEnglish (US)
Pages (from-to)476-493
Number of pages18
JournalCanadian Journal of Statistics
Volume37
Issue number3
DOIs
StatePublished - Sep 2009
Externally publishedYes

Keywords

  • Arfima
  • Bias correction
  • Long memory
  • Sample autocorrelations

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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