Bootstrap‐Based Inference for Cube Root Asymptotics
针对具有Chernoff型极限分布的M估计量，提出一种基于非参数自助法的有效分布近似方法，通过改变准则函数形状恢复一致性，为计量经济学和机器学习中的推断提供通用易行的重抽样方案。
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.