Neural network test and nonparametric kernel test for neglected nonlinearity in regression models

被引：3

作者：

Lee, TH ^{[1
]}

机构：

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

来源：

STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS | 2000年 / 4卷 / 04期

关键词：

asymptotic test; conditional bootstrap; naive bootstrap; recursive bootstrap; wild bootstrap;

D O I：

10.1162/108118200753392109

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This article considers two conditional moment tests for neglected nonlinearity in regression models and examines their finite sample performance. The two tests are the nonparametric kernel test by Li and Wang (1998) and Zheng (1996) and the neural network test of White (1989). The article examines an asymptotic test, a naive bootstrap test, and a wild bootstrap test for weakly dependent time series and independent data.

引用

页码：169 / 182

页数：14

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