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Semiparametric Efficiency Bounds For Conditional Moment Restriction Models With Different Conditioning Variables

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  • Hristache, Marian
  • Patilea, Valentin

Abstract

This paper addresses the problem of semiparametric efficiency bounds for conditional moment restriction models with different conditioning variables. We characterize such an efficiency bound, that in general is not explicit, as a limit of explicit efficiency bounds for a decreasing sequence of unconditional (marginal) moment restriction models. An iterative procedure for approximating the efficient score when this is not explicit is provided. Our theoretical results provide new insight for the theory of semiparametric efficiency bounds literature and open the door to new applications. In particular, we investigate a class of regression-like (mean regression, quantile regression,…) models with missing data, an example of a supply and demand simultaneous equations model and a generalized bivariate dichotomous model.

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  • Hristache, Marian & Patilea, Valentin, 2016. "Semiparametric Efficiency Bounds For Conditional Moment Restriction Models With Different Conditioning Variables," Econometric Theory, Cambridge University Press, vol. 32(4), pages 917-946, August.
    • Handle: RePEc:cup:etheor:v:32:y:2016:i:04:p:917-946_00
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    Cited by:

    1. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
    2. M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.
    3. Hristache, Marian & Patilea, Valentin, 2021. "Equivalent models for observables under the assumption of missing at random," Econometrics and Statistics, Elsevier, vol. 20(C), pages 153-165.
    4. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.

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