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Consistent Specification Testing via Nonparametric Series Regression Author info | Abstract | Publisher info | Download info | Related research | Statistics Hong, Yongmiao
White, Halbert
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This paper proposes two consistent one-sided specification tests for parametric regression models, one based on the sample covariance between the residual from the parametric model and the discrepancy between the parametric and nonparametric fitted values; the other based on the difference in sums of squared residuals between the parametric and nonparametric models. The authors estimate the nonparametric model by series regression. The new test statistics converge in distribution to a unit normal under correct specification and grow to infinity faster than the parametric rate [square root of] n under misspecification, while avoiding weighting, sample splitting, and nonnested testing procedures used elsewhere. Copyright 1995 by The Econometric Society.
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Article provided by Econometric Society in its journal Econometrica .
Volume (Year): 63 (1995)
Issue (Month): 5 (September)
Pages: 1133-59
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Handle: RePEc:ecm:emetrp:v:63:y:1995:i:5:p:1133-59Contact details of provider: Phone: 1 212 998 3820 Fax: 1 212 995 4487 Email: Web page: http://www.econometricsociety.org/ More information through EDIRC
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