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Identification and Estimation of Heteroskedastic Binary Choice Models with Endogenous Dummy Regressors
The Econometrics Journal ( IF 1.9 ) Pub Date : 2018-06-01 , DOI: 10.1111/ectj.12109
Beili Mu 1 , Zhengyu Zhang 1
Affiliation  

In this paper, we consider the semiparametric identification and estimation of a heteroscedastic binary choice model with endogenous dummy regressors and no parametric restriction on the distribution of the error term. Our approach addresses various drawbacks associated with previous estimators proposed for this model. It allows for: general multiplicative heteroscedasticity in both selection and outcome equations; a nonparametric selection mechanism; and multiple discrete endogenous regressors. The resulting three‐stage estimator is shown to be asymptotically normal, with a convergence rate that can be arbitrarily close to n−1/2 if certain smoothness assumptions are satisfied. Simulation results show that our estimator performs reasonably well in finite samples. Our approach is then used to study the intergenerational transmission of smoking habits in British households.

中文翻译:

具有内源虚拟回归变量的异方差二元选择模型的识别和估计

在本文中,我们考虑具有内生虚拟回归变量且对误差项的分布无参数限制的异方差二元选择模型的半参数识别和估计。我们的方法解决了与先前针对该模型提出的估算器相关的各种弊端。它允许:选择方程和结果方程中的一般乘法异方差;非参数选择机制;以及多个离散的内生回归变量。结果表明,三阶段估计量是渐近正态的,如果满足某些平滑度假设,则收敛速度可以任意接近n-1 / 2。仿真结果表明,我们的估计器在有限样本中表现良好。
更新日期:2018-06-01
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