Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: Sep, 2020, Volume 88, Issue 5

Bootstrap-Based Inference for Cube Root Asymptotics
p. 2203-2219

Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa

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.

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Supplemental Material

Supplement to "Bootstrap-Based Inference for Cube Root Asymptotics"

This zip file contains the replication files for the manuscript.

Supplement to "Bootstrap-Based Inference for Cube Root Asymptotics"

This supplemental appendix contains proofs and other theoretical results that may be of independent interest. It also o¤ers more details on the examples and simulation evidence presented in the paper.