Efficient estimation of data combination models by the method of auxiliary-to-study tilting (ast)
By: Bryan S. Graham, Cristine Campos de Xavier Pinto, Daniel Egel
Contributors: Pinto, Campos de Xavier; Egel, Daniel; National Bureau of Economic Research
Series: NBER working paper series -- working paper 16928; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 16928.
Publisher: National Bureau of Economic Research
Published: 2011
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "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.
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