Inverse Probability Tilting for Moment Condition Models with Missing Data
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. Copyright , Oxford University Press.
Authors: | Graham, Bryan S. ; Pinto, Cristine Campos De Xavier ; Egel, Daniel |
---|---|
Published in: |
Review of Economic Studies. - Oxford University Press. - Vol. 79, 3, p. 1053-1079
|
Publisher: |
Oxford University Press |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Inverse Probability Tilting for Moment Condition Models with Missing Data
Graham, Bryan S., (2008)
-
Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)
Graham, Bryan S., (2011)
-
Inverse Probability Tilting for Moment Condition Models with Missing Data
Graham, Bryan S., (2012)
- More ...