Higher order expansions for error variance matrix estimates in the Gaussian AR(1) linear regression model

Yiannis Karavias, Spyridon Symeonides, Elias Tzavalis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

We derive a stochastic expansion of the error variance-covariance matrix estimator
for the linear regression model under Gaussian AR(1) errors. The higher order accuracy terms of the refined formula are not directly derived from formal Edgeworth-type expansions but instead, the paper adopts Magadalinos’ (1992) stochastic order of ω which is a convenient device to obtain the equivalent relation between the stochastic expansion and the asymptotic approximation of corresponding distribution functions. A Monte Carlo experiment compares tests based on the new estimator with others in the literature and shows that the new tests perform well.
Original languageEnglish
Pages (from-to)54-59
Number of pages6
JournalStatistics and Probability Letters
Volume135
Early online date11 Dec 2017
DOIs
Publication statusPublished - 1 Apr 2018

Keywords

  • AR(1) disturbances
  • Asymptotic approximations
  • Autocorrelation robust inference
  • Linear regression
  • Stochastic expansions

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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