Confidence Intervals for Long Memory Regressions

Kyungduk Ko, Boise State University
Jaechoul Lee, Boise State University
Robert Lund, Clemson University

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This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Statistics & Probability Letters, published by Elsevier. Copyright restrictions may apply. doi: 10.1016/j.spl.2008.01.057

Abstract

This paper proposes an accurate condence 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.