Estimation of a Panel Data Sample Selection Model
提出一种两步估计法，用于处理面板数据中同时存在样本选择和个体效应的模型，估计量一致且渐近正态，蒙特卡洛模拟验证了有限样本性质。
We consider the problem of estimation in a panel data sample selection model, where both the selection and the regression equation of interest contain unobservable individual-specific effects. We propose a two-step estimation procedure, which differences out both the sample selection effect and the unobservable individual effect from the equation of interest. In the first step, the unknown coefficients of the selection equation are consistently estimated. The estimates are then used to estimate the regression equation of interest. The estimator proposed in this paper is consistent and asymptotically normal, with a rate of convergence that can be made arbitrarily close to n -1/2 , depending on the strength of certain smoothness assumptions. The finite sample properties of the estimator are investigated in a small Monte Carlo simulation.