Matias D. Cattaneo, Michael Jansson
arXiv 19 Apr 2019 · Econometrics · publishedEconometric Theory (2021) · 7 citations (OpenAlex)
arXiv:1904.09372 · PDF · DOI · OpenAlex · Extracted main text
This paper highlights a tension between semiparametric efficiency and bootstrap consistency in the context of a canonical semiparametric estimation problem, namely the problem of estimating the average density. It is shown that although simple plug-in estimators suffer from bias problems preventing them from achieving semiparametric efficiency under minimal smoothness conditions, the nonparametric bootstrap automatically corrects for this bias and that, as a result, these seemingly inferior estimators achieve bootstrap consistency under minimal smoothness conditions. In contrast, several "debiased" estimators that achieve semiparametric efficiency under minimal smoothness conditions do not achieve bootstrap consistency under those same conditions.
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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 | Giné and Nickl (2008) A Simple Adaptive Estimator of the Integrated Square of a Density | 0.843 | 3 | 3 | 100% |
| 2 | Hall and Marron (1987) Estimation of Integrated Squared Density Derivatives | 0.843 | 3 | 3 | 100% |
| 3 | Giné and Nickl (2008) Uniform Central Limit Theorems for Kernel Density Estimators | 0.811 | 4 | 2 | 100% |
| 4 | Bickel and Ritov (1988) Estimating Integrated Squared Density Derivatives: Sharp Best Order of Convergence Estimates | 0.737 | 3 | 2 | 100% |
| 5 | Cattaneo, Crump, and Jansson (2013) Generalized Jackknife Estimators of Weighted Average Derivatives (With Discussion and Rejoinder) self | 0.644 | 2 | 2 | 100% |
| 6 | Newey and Robins (2018) Cross-Fitting and Fast Remainder Rates for Semiparametric Estimation | 0.644 | 2 | 2 | 100% |
| 7 | Tsybakov (2009) Introduction to Nonparametric Estimation | 0.644 | 2 | 2 | 100% |
| 8 | Ritov and Bickel (1990) Achieving Information Bounds in Non and Semiparametric Models | 0.585 | 3 | 1 | 100% |
| 9 | van der Vaart (1998) Asymptotic Statistics | 0.511 | 2 | 1 | 100% |
| 10 | Belloni, Chernozhukov, Fernández-Val, and Hansen (2017) Program Evaluation and Causal Inference With High-Dimensional Data | 0.405 | 1 | 1 | 100% |
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