Nonparametric bootstrap tests for neglected nonlinearity in time series regression models

被引：12

作者：

Lee, TH ^{[1
]}

Ullah, A ^{[1
]}

机构：

[1] Univ Calif Riverside, Dept Econ, Riverside, CA 92521 USA

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2001年 / 13卷 / 03期

关键词：

nonparametric test; nonlinearity; time series; functional-coefficient model; conditional moment test; naive bootstrap; wild bootstrap; conditional heteroskedasticity; GARCH; Monte carlo;

D O I：

10.1080/10485250108832860

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：

Various nonparametric kernel regression estimators are presented, based on which we consider two nonparametric tests for neglected nonlinearity in time series regression models. One of them is the goodness-of-fit test of Cai, Fan and Yao (2000) and another is the nonparametric conditional moment test by Li and Wang(1998) and Zheng(1996). Bootstrap procedures are used for these tests and their performance is examined via monte carlo experiments, especially with conditionally heteroskedastic errors.

引用

页码：425 / 451

页数：27

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