论文标题
使用非线性转化的内生性校正的渐近性特性
Asymptotic Properties of Endogeneity Corrections Using Nonlinear Transformations
论文作者
论文摘要
本文认为具有内源性回归器的线性回归模型，该模型源于潜在变量的非线性转换。结果表明，可以通过在模型中添加基于等级的回归器转换并执行标准OLS估计来始终如一地估算相应的系数。与其他方法相反，我们的非参数控制功能方法不依赖于规定的副群。此外，该方法允许存在可能（线性）与内源性回归器相关的其他外源回归。证明了估计量的一致性和渐近正态性，并通过蒙特卡洛模拟将估计量与基于Copula的方法进行比较。美国当前人口调查的工资数据的经验应用证明了我们方法的有用性。
This paper considers a linear regression model with an endogenous regressor which arises from a nonlinear transformation of a latent variable. It is shown that the corresponding coefficient can be consistently estimated without external instruments by adding a rank-based transformation of the regressor to the model and performing standard OLS estimation. In contrast to other approaches, our nonparametric control function approach does not rely on a conformably specified copula. Furthermore, the approach allows for the presence of additional exogenous regressors which may be (linearly) correlated with the endogenous regressor(s). Consistency and asymptotic normality of the estimator are proved and the estimator is compared with copula based approaches by means of Monte Carlo simulations. An empirical application on wage data of the US current population survey demonstrates the usefulness of our method.
