Robustness, infinitesimal neighborhoods, and moment restrictions

Yuichi Kitamura, Taisuke Otsu, Kirill Evdokimov

研究成果: Article査読

23 被引用数 (Scopus)

抄録

This paper is concerned with robust estimation under moment restrictions. A moment restriction model is semiparametric and distribution-free; therefore it imposes mild assumptions. Yet it is reasonable to expect that the probability law of observations may have some deviations from the ideal distribution being modeled, due to various factors such as measurement errors. It is then sensible to seek an estimation procedure that is robust against slight perturbation in the probability measure that generates observations. This paper considers local deviations within shrinking topological neighborhoods to develop its large sample theory, so that both bias and variance matter asymptotically. The main result shows that there exists a computationally convenient estimator that achieves optimal minimax robust properties. It is semiparametrically efficient when the model assumption holds, and, at the same time, it enjoys desirable robust properties when it does not.

本文言語English
ページ(範囲)1185-1201
ページ数17
ジャーナルEconometrica
81
3
DOI
出版ステータスPublished - 2013 5
外部発表はい

ASJC Scopus subject areas

  • Economics and Econometrics

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