Robust data-driven inference for density-weighted average derivatives

Matias D. Cattaneo, Richard K. Crump, Michael Jansson

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1070-1083
Number of pages14
JournalJournal of the American Statistical Association
Volume105
Issue number491
DOIs
StatePublished - Sep 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Averaged derivative
  • Bandwidth selection
  • Robust inference
  • Small bandwidth asymptotics

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