Equivalent sample sizes in time series regressions

Jaechoul Lee, Robert Lund

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This article studies confidence intervals for regression parameters in time series settings. An equivalent sample size method is proposed that retains the simple interval structure inherent with white noise model errors, but modifies the sample size to account for the serial autocorrelations present in the errors. This makes the interval perform akin to weighted least squares intervals. The accuracy of the approach is demonstrated in three common regression problems. A noteworthy by-product of the work identifies explicit variances of several classical regression statistics in time series settings.

Original languageEnglish (US)
Pages (from-to)285-297
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume78
Issue number4
DOIs
StatePublished - 2008
Externally publishedYes

Keywords

  • ANOVA
  • Autocorrelation
  • Confidence interval
  • Degrees of freedom
  • Ordinary least squares
  • Regression
  • Time series
  • Weighted least squares

ASJC Scopus subject areas

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

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