Neural Network Test and Nonparametric Kernel Test for Neglected Nonlinearity in Regression Models

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.

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Year of publication:	2007
Authors:	Lee, Tae-Hwy
Published in:	Studies in Nonlinear Dynamics & Econometrics. - Berkeley Electronic Press. - Vol. 4.2007, 4, p. 1063-1063
Publisher:	Berkeley Electronic Press
Subject:	asymptotic test \| conditional bootstrap \| naive bootstrap \| recursive bootstrap \| wild bootstrap

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Extent:	application/pdf
Type of publication:	Article
Notes:	oai:bepress:
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10004966216