A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES
提出一种基于核函数的一致性检验方法，用于检验因变量为分类/离散变量时的条件密度模型设定是否正确，适用于Logit、Probit等常见模型，并支持混合类型协变量。
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.