Staff Reports
Bootstrapping Density-Weighted Average Derivatives
May 2010 Number 452
JEL classification: C12, C14, C21, C24

Authors: Matias D. Cattaneo, Richard K. Crump, and Michael Jansson

Employing the “small-bandwidth” asymptotic framework of Cattaneo, Crump, and Jansson (2009), this paper studies the properties of several bootstrap-based inference procedures associated with a kernel-based estimator of density-weighted average derivatives proposed by Powell, Stock, and Stoker (1989). In many cases, the 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 that employs 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.

Available only in PDF pdf  31 pages / 316 kb
For a published version of this report, see Matias D. Cattaneo, Richard K. Crump, and Michael Jansson, "Bootstrapping Density-Weighted Average Derivatives," Econometric Theory 30, no. 6 (December 2014): 1135-64.
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