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Average Density Estimators: Efficiency and Bootstrap Consistency

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

Abstract

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.

Citation extraction

25
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Giné and Nickl (2008) A Simple Adaptive Estimator of the Integrated Square of a Density0.84333100%
2Hall and Marron (1987) Estimation of Integrated Squared Density Derivatives0.84333100%
3Giné and Nickl (2008) Uniform Central Limit Theorems for Kernel Density Estimators0.81142100%
4Bickel and Ritov (1988) Estimating Integrated Squared Density Derivatives: Sharp Best Order of Convergence Estimates0.73732100%
5Cattaneo, Crump, and Jansson (2013) Generalized Jackknife Estimators of Weighted Average Derivatives (With Discussion and Rejoinder) self0.64422100%
6Newey and Robins (2018) Cross-Fitting and Fast Remainder Rates for Semiparametric Estimation0.64422100%
7Tsybakov (2009) Introduction to Nonparametric Estimation0.64422100%
8Ritov and Bickel (1990) Achieving Information Bounds in Non and Semiparametric Models0.58531100%
9van der Vaart (1998) Asymptotic Statistics0.51121100%
10Belloni, Chernozhukov, Fernández-Val, and Hansen (2017) Program Evaluation and Causal Inference With High-Dimensional Data0.40511100%

Showing the top 10 of 25 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Higher-Order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators0.64422
2Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments0.40511
3Continuous difference-in-differences with double/debiased machine learning0.40511
4Robust Inference for Convex Pairwise Difference Estimators0.40511