Confidence Intervals for Long Memory Regressions

Kyungduk Ko; Jaechoul Lee; Robert Lund

doi:10.1016/j.spl.2008.01.057

Confidence Intervals for Long Memory Regressions

Kyungduk Ko, Jaechoul Lee, Robert Lund

Mathematics Department

Research output: Contribution to journal › Article › peer-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 language	American English
Pages (from-to)	1894-1902
Number of pages	9
Journal	Statistics & Probability Letters
Volume	78
Issue number	13
DOIs	https://doi.org/10.1016/j.spl.2008.01.057
State	Published - 15 Sep 2008

Keywords

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

EGS Disciplines

Mathematics

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10.1016/j.spl.2008.01.057

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title = "Confidence Intervals for Long Memory Regressions",

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.",

keywords = "asymptotic normality, linear regression, long memory, ordinary least squares, weighted least squares",

author = "Kyungduk Ko and Jaechoul Lee and Robert Lund",

year = "2008",

month = sep,

day = "15",

doi = "10.1016/j.spl.2008.01.057",

language = "American English",

volume = "78",

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TY - JOUR

T1 - Confidence Intervals for Long Memory Regressions

AU - Ko, Kyungduk

AU - Lee, Jaechoul

AU - Lund, Robert

PY - 2008/9/15

Y1 - 2008/9/15

N2 - 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.

AB - 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.

KW - asymptotic normality

KW - linear regression

KW - long memory

KW - ordinary least squares

KW - weighted least squares

UR - https://www.scopus.com/pages/publications/49649089538

UR - https://scholarworks.boisestate.edu/math_facpubs/10

U2 - 10.1016/j.spl.2008.01.057

DO - 10.1016/j.spl.2008.01.057

M3 - Article

SN - 0167-7152

VL - 78

SP - 1894

EP - 1902

JO - Statistics & Probability Letters

JF - Statistics & Probability Letters

IS - 13

ER -

Confidence Intervals for Long Memory Regressions

Abstract

Keywords

EGS Disciplines

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