Confidence Intervals for Long Memory Regressions

Kyungduk Ko, Jaechoul Lee, Robert Lund

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper proposes an accurate confidence interval for the trend parameter in a linear regression model with long memory errors. The interval is based upon an equivalent sum of squares method and is shown to perform comparably to a weighted least squares interval. The advantages of the proposed interval lies in its relative ease of computation and should be attractive to practitioners.

Original languageAmerican English
Pages (from-to)1894-1902
Number of pages9
JournalStatistics and Probability Letters
Volume78
Issue number13
DOIs
StatePublished - 15 Sep 2008

Keywords

  • asymptotic normality
  • linear regression
  • long memory
  • ordinary least squares
  • weighted least squares

EGS Disciplines

  • Mathematics

Fingerprint

Dive into the research topics of 'Confidence Intervals for Long Memory Regressions'. Together they form a unique fingerprint.

Cite this