Semiparametric Instrumental Variable Estimation in an Endogenous Treatment Model
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.
|Date of creation:||17 Sep 2015|
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- Klein, Roger W & Spady, Richard H, 1993.
"An Efficient Semiparametric Estimator for Binary Response Models,"
Econometric Society, vol. 61(2), pages 387-421, March.
- Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
- 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. Full references (including those not matched with items on IDEAS)
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