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Bootstrap-Based Inference for Cube Root Asymptotics

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DOI

  • Matias D. Cattaneo, Princeton University
  • ,
  • Michael Jansson
  • Kenichi Nagasawa, University of Warwick

This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting a Chernoff (1964)-type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. The method proposed herein is based on the nonparametric bootstrap, but restores consistency by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy-to-implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our results with four examples in econometrics and machine learning.

Original languageEnglish
JournalEconometrica
Volume88
Issue5
Pages (from-to)2203-2219
Number of pages17
ISSN0012-9682
DOIs
Publication statusPublished - 1 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 The Econometric Society

    Research areas

  • bootstrapping, Cube root asymptotics, empirical risk minimization, maximum score

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