Estimation and Hypothesis Testing in LAV Regression with Autocorrelated Errors: Is Correction for Autocorrelation Helpful?

被引:1
作者
Dielman, Terry E. [1 ,2 ]
机构
[1] Texas Christian Univ, Decis Sci Informat Syst, Ft Worth, TX 76129 USA
[2] Texas Christian Univ, MJ Neeley Sch Business, Management Dept, Ft Worth, TX 76129 USA
关键词
Monte Carlo simulation; serial correlation; Cochrane-Orcutt; Prais-Winsten; lagged variable;
D O I
10.22237/jmasm/1320120720
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Using the Prais-Winsten correction and adding a lagged variable provides improved estimates (smaller MSE) in least absolute value (LAV) regression when moderate to high levels of autocorrelation are present. When comparing empirical levels of significance for hypothesis tests, adding a lagged variable outperforms other approaches but has a relative high empirical level of significance.
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页码:539 / 548
页数:10
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