论文标题
内核加权规范测试在一般分布下
Kernel-weighted specification testing under general distributions
论文作者
论文摘要
内核加权测试统计量已被广泛用于各种设置，包括非平稳回归，倾向得分的推断和面板数据模型。当回归器的定律与Lebesgue度量并非被单数组件污染时，我们为参数条件平均值的基于内核的规范测试开发了极限理论。该结果具有独立的兴趣，并且可能在利用内核平滑U统计数据的其他应用中有用。模拟说明了调节变量分布对测试统计量的功率特性的非平地影响。
Kernel-weighted test statistics have been widely used in a variety of settings including non-stationary regression, inference on propensity score and panel data models. We develop the limit theory for a kernel-based specification test of a parametric conditional mean when the law of the regressors may not be absolutely continuous to the Lebesgue measure and is contaminated with singular components. This result is of independent interest and may be useful in other applications that utilize kernel smoothed U-statistics. Simulations illustrate the non-trivial impact of the distribution of the conditioning variables on the power properties of the test statistic.
