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.
This is an author-produced, peer-reviewed version of this article. © 2009, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). The final, definitive version of this document can be found online at Statistics & Probability Letters, doi: 10.1016/j.spl.2008.01.057
Ko, Kyungduk; Lee, Jaechoul; and Lund, Robert. (2008). "Confidence Intervals for Long Memory Regressions". Statistics & Probability Letters, 78(13), 1894-1902.