Department of Economics and Business Economics

Bootstrapping Density-Weighted Average Derivatives

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  • Rp10 23

    Final published version, 369 KB, PDF document

  • Matias D. Cattaneo, University of Michigan, United States
  • Richard K. Crump, Federal Reserve Bank of New York, United States
  • Michael Jansson
  • School of Economics and Management
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 estimator
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages30
Publication statusPublished - 2010

    Research areas

  • Averaged derivatives, Bootstrap, Small bandwidth asymptotics

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