Efficient estimation of data combination models by the method of Auxiliary-to-Study Tilting (AST)

Resumo

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.

Ficha do documento

Tipo

Artigo científico

Ano

2016

Instituição

Amer Statistical Assoc

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso restrito

DOI

10.1080/07350015.2015.1038544

Identificador

oai:repositorio.fgv.br:10438/23573

Temas

EconomiaDados

Palavras-chave

Propensity scoreEarnings decompositionsTwo-sample instrumental variables (TSIV)Double robustnessAverage treatment effect on the treated (ATT)Semiparametric difference-in-differencesEconomiaRobustez (Estatística)