Confidence Intervals for Long Memory Regressions
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
This paper proposes an accurate condence 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.
Kyungduk Ko, Jaechoul Lee, and Robert Lund. "Confidence Intervals for Long Memory Regressions" Statistics & Probability Letters (2008).
Available at: http://works.bepress.com/kyungduk_ko/4