Bootstrap-Based Inference for Cube Root Asymptotics

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Jansson, Michael; Nagasawa, Kenichi
• 署名单位: Princeton University; University of California System; University of California Berkeley; CREATES; University of Warwick
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA17950
• 发表日期: 2020
• 页码: 2203-2219
• 关键词: SEMIPARAMETRIC ANALYSIS; M-ESTIMATORS; maximum; models

摘要

This paper proposes a valid bootstrap-based distributional approximation forM-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.