Robust Data-Driven Inference for Density-Weighted Average Derivatives

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael
• 署名单位: University of Michigan System; University of Michigan; Federal Reserve System - USA; Federal Reserve Bank - New York; University of California System; University of California Berkeley; CREATES
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2010.tm09590
• 发表日期: 2010
• 页码: 1070-1083
• 关键词: semiparametric estimation; bandwidth choice

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.