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A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES

Published online by Cambridge University Press:  30 July 2012

Li Cong  and
Jeffrey S. Racine
Li Cong*
Affiliation:
Shanghai University of Finance and Economics
Jeffrey S. Racine*
Affiliation:
McMaster University
*
*Address correspondence to Jeffrey S. Racine, Department of Economics, McMaster University, Hamilton, ON, Canada L8S 4M4; e-mail: [email protected].
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Abstract

We propose a consistent kernel-based specification test for conditional density models when the dependent variable is categorical/discrete. The method is applicable to popular parametric binary choice models such as the logit and probit specification and their multinomial and ordered counterparts, along with parametric count models, among others. The test is valid when the conditional density function contains both categorical and real-valued covariates. Theoretical support for the test and for a bootstrap-based version of the test is provided. Monte Carlo simulations are conducted to assess the finite-sample performance of the proposed method.

Type
Miscellanea
Information
Econometric Theory , Volume 29 , Issue 3 , June 2013 , pp. 629 - 641
Copyright
Copyright © Cambridge University Press 2012 

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Footnotes

Racine would like to acknowledge the generous support of SHARCNET (www.sharcnet.ca).

References

REFERENCES

Aitchison, J. & Aitken, C.G.G. (1976) Multivariate binary discrimination by the kernel method. Biometrika 63, 413–420.CrossRefGoogle Scholar
Fan, Y., Li, Q., & Min, I. (2006) A nonparametric bootstrap test of conditional distributions. Econometric Theory 22, 587–613.CrossRefGoogle Scholar
Hall, P., Racine, J., & Li, Q. (2004) Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association 99, 1015–1026.CrossRefGoogle Scholar
Hausman, J. & McFadden, D. (1984) Specification tests for the multinomial logit model. Econometrica 52(5), 1219–1240.CrossRefGoogle Scholar
Mora, J. & Moro-Egido, A.I. (2008) On specification testing of ordered discrete choice models. Journal of Econometrics 143, 191–205.CrossRefGoogle Scholar
Rodriguez-Poo, J.M., Sperlich, S., & Vieu, P. (2004) An adaptive specification test for semiparametric models. SSRN eLibrary.Google Scholar
Wang, M.C. & van Ryzin, J. (1981) A class of smooth estimators for discrete distributions. Biometrika 68, 301–309.CrossRefGoogle Scholar
Zheng, J.X. (2000) A consistent test of conditional parametric distributions. Econometric Theory 16, 667–691.CrossRefGoogle Scholar