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

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  • Lee Tae-Hwy

    (University of California, Riverside)

Abstract

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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File URL: http://www.degruyter.com/view/j/snde.2001.4.4/snde.2001.4.4.1063/snde.2001.4.4.1063.xml?format=INT
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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 4 (2001)
Issue (Month): 4 (January)
Pages: 1-15

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Handle: RePEc:bpj:sndecm:v:4:y:2001:i:4:n:1

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Cited by:
  1. Shintani, Mototsugu, 2008. "A dynamic factor approach to nonlinear stability analysis," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 32(9), pages 2788-2808, September.
  2. Shintani, Mototsugu, 2005. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 37(3), pages 517-38, June.

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