Inverse Probability Tilting for Moment Condition Models with Missing Data

Bryan Graham, Cristine Pinto and Daniel Egel

No 13981, NBER Working Papers from National Bureau of Economic Research, Inc

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 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.

JEL-codes: C14 C21 C23 (search for similar items in EconPapers)
Date: 2008-05
New Economics Papers: this item is included in nep-ecm
Note: TWP
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Citations: View citations in EconPapers (7)

Published as Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.

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Journal Article: Inverse Probability Tilting for Moment Condition Models with Missing Data (2012)
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