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Econometrica Vol. 88 No. 5 2020

Bootstrap‐Based Inference for Cube Root Asymptotics

Matias D. Cattaneo1; Michael Jansson2,3; Kenichi Nagasawa4

1 Department of Operations Research and Financial Engineering, Princeton University · 2 Department of Economics, University of California at Berkeley · 3 CREATES · 4 Department of Economics, University of Warwick

open access

Abstract

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.

DOI
10.3982/ecta17950
Volume
88
Issue
5
Pages
2203-2219
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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