Testing Serial Correlation in Partial Linear Errors-in-Variables Models Based on Empirical Likelihood

被引：15

作者：

Liu, Feng ^{[2
]}

Chen, Gemai ^{[3
]}

Chen, Min ^{[1
]}

机构：

[1] Chinese Acad Sci, Acad Mathemat & Syst Sci, Beijing 100080, Peoples R China

[2] Chongqing Inst Technol, Dept Stat, Chongqing, Peoples R China

[3] Univ Calgary, Dept Mathemat & Stat, Calgary, AB, Canada

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2008年 / 37卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Empirical likelihood; Errors-in-variables; Partial linear model; Serial correlation; Wilks' theorem;

D O I：

10.1080/03610920801893780

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：

In this article, we propose an empirical likelihood-based test to check the existence of serial correlation in partial linear errors-in-variables models. A nonparametric version of Wilks' theorem is derived, which says that our proposed test has an asymptotic chi-square distribution. Simulation results reveal that the finite sample performance of our proposed test is satisfactory in both size and power.

引用

页码：1905 / 1918

页数：14

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