Inverse Probability Tilting for Moment Condition Models with Missing Data
提出一种新的逆概率加权估计量，用于处理缺失数据下的矩条件模型，在效率、稳健性和高阶偏差上优于现有方法，并以早期认知成就差异对成年收入差异的影响为例进行说明。
We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favourably with existing IPW estimators, including augmented IPW estimators, in terms of efficiency, robustness, and higher-order bias. We illustrate our method with a study of the relationship between early Black–White differences in cognitive achievement and subsequent differences in adult earnings. In our data set, the early childhood achievement measure, the main regressor of interest, is missing for many units.