全球智库信息集成服务系统(G2TT): Inverse Probability Tilting for Moment Condition Models with Missing Data

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w13981
来源ID	Working Paper 13981
	Inverse Probability Tilting for Moment Condition Models with Missing Data
	Bryan S. Graham; Cristine Campos de Xavier Pinto; Daniel Egel
发表日期	2008-05-09
出版年	2008
语种	英语
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 favorably with existing IPW estimators, including augmented inverse probability weighting (AIPW) 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 dataset the early childhood achievement measure, the main regressor of interest, is missing for many units.
主题	Econometrics ; Estimation Methods
URL	https://www.nber.org/papers/w13981
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/571656
推荐引用方式 GB/T 7714	Bryan S. Graham,Cristine Campos de Xavier Pinto,Daniel Egel. Inverse Probability Tilting for Moment Condition Models with Missing Data. 2008.