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Semiparametric and nonparametric estimation of sample selection models under symmetry

机译:对称条件下样本选择模型的半参数和非参数估计

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摘要

This paper considers the semiparametric estimation of binary choice sample selection models under a joint symmetry assumption. Our approaches overcome various drawbacks associated with existing estimators. In particular, our method provides root-n consistent estimators for both the intercept and slope parameters of the outcome equation in a heteroscedastic framework, without the usual cross equation exclusion restriction or parametric specification for the error distribution and/or the form of heteroscedasticity. Our two-step estimators are shown to be consistent and asymptotically normal. A Monte Carlo simulation study indicates the usefulness of our approaches.
机译:本文考虑联合对称假设下二元选择样本选择模型的半参数估计。我们的方法克服了与现有估计器相关的各种缺点。特别地,我们的方法为异方差框架中结果方程的截距和斜率参数提供了根n一致的估计量,而没有通常的交叉方程排除约束或参数规范(针对误差分布和/或异方差形式)。我们的两步估计量被证明是一致且渐近正态的。蒙特卡洛模拟研究表明了我们方法的有效性。

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