Efficient estimation of data combination models by the method of auxiliary-to-study tilting (ast) / Bryan S. Graham, Cristine Campos de Xavier Pinto, Daniel Egel.

Author/creator Graham, Bryan S.
Other author Egel, Daniel.
Other author Pinto, Campos de Xavier.
Other author National Bureau of Economic Research.
Format Electronic
Publication InfoCambridge, MA : National Bureau of Economic Research,
Supplemental ContentFull text available from NBER Working Papers

SeriesNBER working paper series ; working paper 16928
Working paper series (National Bureau of Economic Research : Online) ; working paper no. 16928. UNAUTHORIZED
Summary "We propose a locally efficient, doubly robust, 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 analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case). Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, and poverty maps. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996). We find that residual Black-White inequality is negligible at lower and higher quantiles of the Black wage distribution, but substantial at middle quantiles"--National Bureau of Economic Research web site.
General noteTitle from PDF file as viewed on 6/16/2011.
Bibliography noteIncludes bibliographical references.
Access restrictionAvailable only to authorized users.
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Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2011657160

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