A NONPARAMETRIC TEST OF SIGNIFICANT VARIABLES IN GRADIENTS
提出一种非参数检验方法，用于识别回归均值函数偏导数中的显著变量，通过局部多项式估计和变异度量构造检验统计量，并利用野生自助法进行推断。
We propose a nonparametric test of significant variables in the partial derivative of a regression mean function. The derivative is estimated by local polynomial estimation and the test statistic is constructed through a variation-based measure of the derivative in the direction of variables of interest. We establish the asymptotic null distribution of the test statistic and demonstrate that it is consistent. Motivated by the null distribution, we propose a wild bootstrap test, and show that it exhibits the same null distribution, whether the null is valid or not. We perform a Monte Carlo study to demonstrate its encouraging finite sample performance. An empirical application is conducted showing how the test can be applied to infer certain aspects of regression structures in a hedonic price model.