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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: racinej@mcmaster.ca.
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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.

Information

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

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