Testing Conditional Independence Restrictions
提出一种基于经验分布函数推广的非参数检验，用于检验变量间的条件独立性假设，适用于离散变量和估计参数，并通过蒙特卡洛模拟验证其有效性。
We propose a nonparametric test of the hypothesis of conditional independence between variables of interest based on a generalization of the empirical distribution function. This hypothesis is of interest both for model specification purposes, parametric and semiparametric, and for nonmodel-based testing of economic hypotheses. We allow for both discrete variables and estimated parameters. The asymptotic null distribution of the test statistic is a functional of a Gaussian process. A bootstrap procedure is proposed for calculating the critical values. Our test has power against alternatives at distance n −1/2 from the null; this result holding independently of dimension. Monte Carlo simulations provide evidence on size and power.