Weighted quantile regression for AR model with infinite variance errors

Zhao Chen, Runze Li, Yaohua Wu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Autoregressive (AR) models with finite variance errors have been well studied. This paper is concerned with AR models with heavy-tailed errors, which is useful in various scientific research areas. Statistical estimation for AR models with infinite variance errors is very different from those for AR models with finite variance errors. In this paper, we consider a weighted quantile regression for AR models to deal with infinite variance errors. We further propose an induced smoothing method to deal with computational challenges in weighted quantile regression. We show that the difference between weighted quantile regression estimate and its smoothed version is negligible. We further propose a test for linear hypothesis on the regression coefficients. We conduct Monte Carlo simulation study to assess the finite sample performance of the proposed procedures. We illustrate the proposed methodology by an empirical analysis of a real-life data set.

Original languageEnglish (US)
Pages (from-to)715-731
Number of pages17
JournalJournal of Nonparametric Statistics
Volume24
Issue number3
DOIs
StatePublished - Sep 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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