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Semiparametric Instrumental Variable Estimation in an Endogenous Treatment Model

Listed author(s):
  • Roger Klein

    ()

    (Rutgers University)

  • Chan Shen

    ()

    (UT MD Anderson)

Registered author(s):

    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.

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    File URL: http://www.sas.rutgers.edu/virtual/snde/wp/2015-11.pdf
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    Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201511.

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    Length: 49 pages
    Date of creation: 17 Sep 2015
    Handle: RePEc:rut:rutres:201511
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    Web page: http://economics.rutgers.edu/

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    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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