Inverse Probability Tilting for Moment Condition Models with Missing Data

Bryan S. Graham, Cristine Campos de Xavier Pinto, Daniel Egel

NBER Working Paper No. 13981
Issued in May 2008
NBER Program(s):   TWP

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.

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Published: 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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This paper was revised on December 5, 2011

Acknowledgments

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