Inverse Probability Tilting for Moment Condition Models with Missing Data

Bryan S. Graham; C. C. De Xavier Pinto; D. Egel

doi:10.1093/restud/rdr047

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Review of Economic Studies Vol. 79 No. 3 2012

Inverse Probability Tilting for Moment Condition Models with Missing Data

Bryan S. Graham¹; C. C. De Xavier Pinto; D. Egel²

¹ University of California, Berkeley · ² RAND Corporation

Abstract

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.

DOI: 10.1093/restud/rdr047
Volume: 79
Issue: 3
Pages: 1053-1079
Language: en
Sources: bibtex:phds-export.bib openalex crossref

Inverse Probability Tilting for Moment Condition Models with Missing Data

Abstract

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