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A smooth nonparametric conditional density test for categorical responses created by Cong Li and Jeffrey S. Racine

By: Contributor(s): Material type: TextTextSeries: Econometric Theory ; Volume 29, number 3,Cambridge: Cambridge University Press, 2013Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISSN:
  • 02664666
Subject(s): LOC classification:
  • HB139.T52 ECO
Online resources: 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.
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Item type Current library Call number Vol info Copy number Status Notes Date due Barcode
Journal Article Journal Article Main Library Journal Article HB139.T52 ECO (Browse shelf(Opens below)) Vol. 29, no.3 (pages 629-641) SP17539 Not for loan For In House Use Only

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.

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