A smooth nonparametric conditional density test for categorical responses
Li, Cong
A smooth nonparametric conditional density test for categorical responses created by Cong Li and Jeffrey S. Racine - Econometric Theory Volume 29, number 3, .
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.
02664666
Nonparametric statistics--Estimation theory--Statistical distribution
Statistical test
HB139.T52 ECO
A smooth nonparametric conditional density test for categorical responses created by Cong Li and Jeffrey S. Racine - Econometric Theory Volume 29, number 3, .
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.
02664666
Nonparametric statistics--Estimation theory--Statistical distribution
Statistical test
HB139.T52 ECO