Improving the bandwidth-free inference methods by prewhitening

Yeonwoo Rho, Xiaofeng Shao

Research output: Contribution to journalArticle

Abstract

In this paper we consider inference of parameters in time series regression models. In the traditional inference approach, the heteroskedasticity and autocorrelation consistent (HAC) estimation is often involved to consistently estimate the asymptotic covariance matrix of regression parameter estimator. Since the bandwidth parameter in the HAC estimation is difficult to choose in practice, there has been a recent surge of interest in developing bandwidth-free inference methods. However, existing simulation studies show that these new methods suffer from severe size distortion in the presence of strong temporal dependence for a medium sample size. To remedy the problem, we propose to apply the prewhitening to the inconsistent long-run variance estimator in these methods to reduce the size distortion. The asymptotic distribution of the prewhitened Wald statistic is obtained and the general effectiveness of prewhitening is shown through simulations.

Original languageEnglish (US)
Pages (from-to)1912-1922
Number of pages11
JournalJournal of Statistical Planning and Inference
Volume143
Issue number11
DOIs
StatePublished - Nov 1 2013

Keywords

  • Prewhitening
  • Robust testing
  • Self-normalization
  • Time series regression

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Improving the bandwidth-free inference methods by prewhitening'. Together they form a unique fingerprint.

  • Cite this