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Bootstrapping density-weighted average derivatives

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

Abstract

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.

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Bibliographic Info

Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 452.

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Date of creation: 2010
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Handle: RePEc:fip:fednsr:452

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Related research

Keywords: Statistical methods ; Econometrics ; Econometrics - Asymptotic theory ; Econometric models;

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Cited by:
  1. Yulia Kotlyarova & Marcia M Schafgans & Victoria Zinde-Walsh, 2011. "Adapting Kernel Estimation to Uncertain Smoothness," STICERD - Econometrics Paper Series /2011/557, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  2. Clara Lia Machado & Carlos León & Miguel Sarmiento & Orlando Chipatecua, 2010. "Riesgo Sistémico y Estabilidad del Sistema de Pagos de Alto Valor en Colombia: Análisis bajo Topología de Redes y Simulación de Pagos," BORRADORES DE ECONOMIA 007669, BANCO DE LA REPÚBLICA.

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