Revisiting simple linear regression with autocorrelated errors

Jaechoul Lee, Robert Lund

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

This paper studies properties of ordinary and generalised least squares estimators in a simple linear regression with stationary autocorrelated errors. Explicit expressions for the variances of the regression parameter estimators are derived for some common time series autocorrelation structures, including a first-order autoregression and general moving averages. Applications of the results include confidence intervals and an example where the variance of the trend slope estimator does not increase with increasing autocorrelation.

Original languageEnglish (US)
Pages (from-to)240-245
Number of pages6
JournalBiometrika
Volume91
Issue number1
DOIs
StatePublished - 2004
Externally publishedYes

Keywords

  • Generalised least squares
  • Ordinary least squares
  • Simple linear regression
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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