Robust Data-Driven Inference for Density-Weighted Average Derivatives

被引：17

作者：

Cattaneo, Matias D. ^{[1
]}

Crump, Richard K. ^{[2
]}

Jansson, Michael ^{[3
,4
]}

机构：

[1] Univ Michigan, Dept Econ, Ann Arbor, MI 48109 USA

[2] Fed Reserve Bank New York, New York, NY 10045 USA

[3] Univ Calif Berkeley, Dept Econ, Berkeley, CA 94720 USA

[4] CREATES, Berkeley, CA 94720 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2010年 / 105卷 / 491期

基金：

新加坡国家研究基金会; 美国国家科学基金会;

关键词：

Averaged derivative; Bandwidth selection; Robust inference; Small bandwidth asymptotics; SEMIPARAMETRIC ESTIMATION; BANDWIDTH CHOICE;

D O I：

10.1198/jasa.2010.tm09590

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo. Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study.

引用

页码：1070 / 1083

页数：14