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Over-Identified Doubly Robust Identification and Estimation

Author

Listed:
  • Arthur Lewbel

    (Boston College)

  • Jin-Young Choi

    (Xiamen University)

  • Zhuzhu Zhou

    (Boston College)

Abstract

Consider two parametric models. At least one is correctly specified, but we don't know which. Both models include a common vector of parameters. An estimator for this common parameter vector is called Doubly Robust (DR) if it's consistent no matter which model is correct. We provide a general technique for constructing DR estimators (assuming the models are over identified). Our Over-identified Doubly Robust (ODR) technique is a simple extension of the Generalized Method of Moments. We illustrate our ODR with a variety of models. Our empirical application is instrumental variables estimation, where either one of two instrument vectors might be invalid.

Suggested Citation

  • Arthur Lewbel & Jin-Young Choi & Zhuzhu Zhou, 2019. "Over-Identified Doubly Robust Identification and Estimation," Boston College Working Papers in Economics 1003, Boston College Department of Economics, revised 01 Feb 2021.
  • Handle: RePEc:boc:bocoec:1003
    Note: previously circulated as "General Doubly Robust Identification and Estimation"
    as

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    References listed on IDEAS

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    Cited by:

    1. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.

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    More about this item

    Keywords

    Doubly Robust Estimation; Generalized Method of Moments; Instrumental Variables; Average Treatment Effects; Parametric Models;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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