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Semiparametric Instrumental Variable Estimation in an Endogenous Treatment Model


  • Roger Klein

    () (Rutgers University)

  • Chan Shen

    () (UT MD Anderson)


We propose instrumental variable(IV) estimators for quantile marginal effects and the parameters upon which they depend in a semiparametric outcome model with endogenous discrete treatment variables. We prove identification, consistency, and asymptotic normality of the estimators. We also show that they are efficient under correct model specification. Further, we show that they are robust to misspecification of the treatment model in that consistency and asymptotic normality continue to hold in this case. In the Monte Carlo study, the estimators perform well over diverse designs covering both correct and incorrect treatment model specifications.

Suggested Citation

  • Roger Klein & Chan Shen, 2015. "Semiparametric Instrumental Variable Estimation in an Endogenous Treatment Model," Departmental Working Papers 201511, Rutgers University, Department of Economics.
  • Handle: RePEc:rut:rutres:201511

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    References listed on IDEAS

    1. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    2. Amemiya, Takeshi, 1977. "The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model," Econometrica, Econometric Society, vol. 45(4), pages 955-968, May.
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    More about this item


    semiparametric; IV; marginal effects; efficiency; robustness;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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