A CONSISTENT TEST OF CONDITIONAL PARAMETRIC DISTRIBUTIONS
提出一种基于Kullback-Leibler信息函数一阶线性展开和核估计的非参数检验，用于判断条件参数分布是否设定正确，适用于经济学等领域的模型检验。
This paper proposes a new nonparametric test for conditional parametric distribution functions based on the first-order linear expansion of the Kullback–Leibler information function and the kernel estimation of the underlying distributions. The test statistic is shown to be asymptotically distributed standard normal under the null hypothesis that the parametric distribution is correctly specified, whereas asymptotically rejecting the null with probability one if the parametric distribution is misspecified. The test is also shown to have power against any local alternatives approaching the null at rates slower than the parametric rate n −1/2 . The finite sample performance of the test is evaluated via a Monte Carlo simulation.