Matching As An Econometric Evaluation Estimator
发展了匹配作为计量经济学评估估计量的方法，给出了基于核匹配的严格分布理论，并扩展了倾向得分匹配的适用条件，发现即使已知倾向得分，使用它也不一定降低估计量方差。
This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic. We focus on the method of propensity score matching and show that it is not necessarily better, in the sense of reducing the variance of the resulting estimator, to use the propensity score method even if propensity score is known. We extend the statistical literature on the propensity score by considering the case when it is estimated both parametrically and nonparametrically. We examine the benefits of separability and exclusion restrictions in improving the efficiency of the estimator. Our methods also apply to the econometric selection bias estimator.