Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
arXiv 26 Apr 2017 · Mathematics — Statistics Theory · publishedEconometrica (2020) · 28 citations (OpenAlex)
arXiv:1704.08066 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pollard (1989) Asymptotics via Empirical Processes | 0.874 | 10 | 2 | 100% |
| 2 | Kim and Pollard (1990) Cube Root Asymptotics | 0.811 | 4 | 2 | 100% |
| 3 | Mohammadi and van de Geer (2005) Asymptotics in Empirical Risk Minimization | 0.693 | 7 | 1 | 100% |
| 4 | Honoré and Kyriazidou (2000) Panel Data Discrete Choice Models with Lagged Dependent Variables | 0.511 | 2 | 1 | 100% |
| 5 | Abrevaya and Huang (2005) On the Bootstrap of the Maximum Score Estimator | 0.405 | 1 | 1 | 100% |
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