论文标题：A Nonparametric Test of Significant Variables in Gradients

　　发表时间：2020.11 online

　　论文所有作者：Feng Yao, Taining Wang

　　期刊名及所属分类：Econometric Theory(国际A-)

　　英文摘要：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.

　　中文摘要：我们提出了回归均值函数的偏导数中重要变量的非参数检验。该方法通过局部多项式估计来估计导数，并通过变量方向上导数的一种基于变分的度量来构造检验统计量。我们建立了检验统计量的渐近零分布，并证明了它是一致的。受null分布的影响，我们提出了一个狂野的自举测试，并表明无论null是否有效，它都表现出相同的null分布。我们进行蒙特卡罗研究，以证明其令人鼓舞的有限样本性能。一个实证的应用是进行了展示如何可以应用测试来推断回归结构的某些方面在享乐价格模型。