非参数选择机制下二值因变量的内生选择模型的估计

Estimation of a binary dependent variable sample selection model with endogeneity under a nonparametric selection mechanism

本文基于非参数的选择机制,探讨一类二值因变量的内生样本选择模型的估计问题.本文在模型设定时,既不对方程中误差项的分布作具体假设,也不设定选择方程中的函数形式,从而大大减少了模型误设的可能性.本文给出模型的识别条件,并提出两步半参数极大似然估计方法,其中第一步是针对选择方程进行非参数回归,第二步是基于选择方程中的估计结果,针对结果方程中的参数进行估计.本文利用控制函数方法来解决模型中可能.存在的内生性,并给出估计量的一致性和渐近正态性质.通过Monte Carlo模拟与文献中其他方法进行了比较,结果表明,本文的方法在模型稳健性方面具有较为明显的优势.最后,将估计方法应用于研究劳动力市场上女性的就业模式这一社会现实问题,体现了本文方法的实用价值.

In this paper,we consider an estimation of a sample selection model with a binary dependent variable and possible endogenous regressors.Under the semiparametric framework,we impose neither the parametric specification of the error distribution nor the functional form of the selection equation,which largely reduces the risk of model misspecification.Throughout this paper,we present the identification conditions and propose a two-step semiparametric maximum likelihood estimator with the first step being a nonparametric regression for the selection variable.A control function approach is used to control for the possible endogeneity.The proposed estimator is shown to be consistent and asymptotically normal.In the simulation studies,we compare the finite sample properties of our estimator with those of existing alternatives,demonstrating the significant advantages of our approach especially in robustness to the model misspecificat ion.Finally,an economic application on labor economics is carried out to illustrate the usefulness of our estimator.