Practical procedures to deal with common support problems in matching estimation
研究了匹配和回归等估计量在面临共同支持问题时的表现，发现缺乏共同支持会增加所有参数和半参数估计的均方根误差，而剔除支持外的观测通常能改善估计性能。
This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with the so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an empirical Monte Carlo simulation design, a lack of common support is found to increase the root-mean-squared error of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.