Quantile inference for near-integrated autoregressive time series with infinite variance

Ngai Hang Chan, Liang Peng, Yongcheng Qi

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The limiting distribution of the quantile estimate for the autoregressive coefficient of a near-integrated first order autoregressive model with infinite variance errors is derived. Since the limiting distribution depends on the unknown density function of the errors, an empirical likelihood ratio statistic is proposed from which confidence intervals can be constructed for the near unit root model without knowing the density function. Numerical simulations are conducted to compare the performance of the empirical likelihood method and the least squares procedure. It is found that the empirical likelihood method outperforms the least squares procedure in general.

Original languageEnglish (US)
Pages (from-to)15-28
Number of pages14
JournalStatistica Sinica
Volume16
Issue number1
StatePublished - Jan 2006

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

  • Empirical likelihood method
  • Infinite variance
  • Near unit root
  • Quantile estimate

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