HAC Corrections for Strongly Autocorrelated Time Series

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36 Scopus citations

Abstract

Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I assume that the long-run properties can be approximated by a stationary Gaussian AR(1) model, with coefficient arbitrarily close to one. In this setting, I derive tests that come close to maximizing a weighted average power criterion. Small sample simulations show these tests to perform well, also in a regression context.

Original languageEnglish (US)
Pages (from-to)311-322
Number of pages12
JournalJournal of Business and Economic Statistics
Volume32
Issue number3
DOIs
StatePublished - Jul 3 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • AR(1)
  • Local-to-unity
  • Long-run variance

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