.png){kind=icon}
.png){kind=icon}
Return
Robust Data-Driven Inference for Density-Weighted Average Derivatives
.png){kind=icon}
OA
AI
DOI：10.1198/jasa.2010.tm09590.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Averaged derivative
Bandwidth selection
Robust inference
Small bandwidth asymptotics
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3
Papers:
5.1K
Citations:
4.8W
Organization
View more organizations