Inverse Probability Tilting for Moment Condition Models with Missing Data

• 成果类型: Article
• 署名作者: Graham, Bryan S.; De Xavier Pinto, Cristine Campos; Egel, Daniel
• 署名单位: University of California System; University of California Berkeley; National Bureau of Economic Research; Getulio Vargas Foundation; RAND Corporation
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdr047
• 发表日期: 2012
• 页码: 1053-1079
• 关键词: doubly robust estimation; Semiparametric Efficiency; Empirical Likelihood; gmm; estimators

摘要

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.