Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)

Bryan Graham, Cristine Pinto and Daniel Egel

Journal of Business & Economic Statistics, 2016, vol. 34, issue 2, 288-301

Abstract: We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, poverty maps, and semiparametric difference-in-differences. In an empirical application, we use our procedure to characterize residual Black--White wage inequality after flexibly controlling for “premarket” differences in measured cognitive achievement. Supplementary materials for this article are available online.

Date: 2016
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Working Paper: Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST) (2011)
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DOI: 10.1080/07350015.2015.1038544

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