Efficient semiparametric copula estimation of regression models with endogeneity
提出一种基于Copula的筛极大似然估计方法，用于处理回归模型中的内生性问题，通过参数Copula和非参数边际分布刻画内生变量与误差项的联合分布，并证明了半参数有效性。
An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.