Average Density Estimators: Efficiency and Bootstrap Consistency

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Date

19 avril 2019

Type de document
Périmètre
Identifiant
  • 1904.09372
Collection

arXiv

Organisation

Cornell University



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Bias

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Matias D. Cattaneo et al., « Average Density Estimators: Efficiency and Bootstrap Consistency », arXiv - économie


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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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