Robustness, infinitesimal neighborhoods, and moment restrictions

Yuichi Kitamura, Taisuke Otsu, Kirill Evdokimov

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1185-1201
Number of pages17
Issue number3
StatePublished - May 2013

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Asymptotic Minimax Theorem
  • Hellinger distance
  • Semiparametric efficiency


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