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英文文献：Bootstrapping Density-Weighted Average Derivatives-Bootstrapping密度加权平均导数
英文文献作者：Matias D. Cattaneo,Richard K. Crump,Michael Jansson
英文文献摘要：
Employing the "small bandwidth" asymptotic framework of Cattaneo, Crump, and Jansson (2009), this paper studies the properties of a variety of bootstrap-based inference procedures associated with the kernel-based density-weighted averaged derivative estimator proposed by Powell, Stock, and Stoker (1989). In many cases validity of bootstrap-based inference procedures is found to depend crucially on whether the bandwidth sequence satisfies a particular (asymptotic linearity) condition. An exception to this rule occurs for inference procedures involving a studentized estimator employing a "robust" variance estimator derived from the "small bandwidth" asymptotic framework. The results of a small-scale Monte Carlo experiment are found to be consistent with the theory and indicate in particular that sensitivity with respect to the bandwidth choice can be ameliorated by using the "robust"variance estimatorClassification-JEL: C12, C14, C21, C24
利用Cattaneo、Crump和Jansson(2009)的“小带宽”渐近框架，本文研究了与Powell、Stock和Stoker(1989)提出的基于核的密度加权平均导数估计量相关的各种基于bootstrap的推理程序的性质。在许多情况下，基于bootstrap的推理程序的有效性被发现在很大程度上取决于带宽序列是否满足一个特定的(渐近线性)条件。此规则的一个例外发生在涉及使用“小带宽”渐近框架导出的“稳健”方差估计器的研究估计器的推理过程。小规模蒙特卡洛实验的结果发现与理论是一致的，并特别表明，有关带宽选择的敏感性可以通过使用“稳健的”方差估计分类jel: C12, C14, C21, C24得到改善
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