A Consistent Test for Multivariate Conditional Distributions
提出一种检验多元参数条件分布的新方法，该检验在原假设下渐近正态，对所有固定备择假设一致，且对局部备择假设有非平凡功效。蒙特卡洛模拟显示，在单变量和多变量模型中，即使面对高持久性依赖数据，该检验在常见样本量下也有合理的检验水平和良好功效。
We propose a new test for a multivariate parametric conditional distribution of a vector of variables y t given a conditional vector x t . The proposed test is shown to have an asymptotic normal distribution under the null hypothesis, while being consistent for all fixed alternatives, and having nontrivial power against a sequence of local alternatives. Monte Carlo simulations show that our test has reasonable size and good power for both univariate and multivariate models, even for highly persistent dependent data with sample sizes often encountered in empirical finance.