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dc.contributor.authorReichert, Arndt-
dc.contributor.authorTauchmann, Harald-
dc.date.accessioned2012-09-27T15:12:23Z-
dc.date.available2012-09-27T15:12:23Z-
dc.date.issued2012-09-27-
dc.identifier.urihttp://hdl.handle.net/2003/29648-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5377-
dc.description.abstractThe classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inconsistent if an interaction of the outcome variable and an explanatory variable matters for selection. To address this specification problem, a full information maximum likelihood estimator and a simple two-step estimator are developed. Monte-Carlo simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and the ordinary heckit procedure, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for weight loss among the obese. Estimation results indicate that the choice of the estimation procedure clearly matters.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;40/2012-
dc.subjectgeneralized estimatoren
dc.subjectheterogeneityen
dc.subjectinteractionen
dc.subjectselection biasen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleWhen outcome heterogeneously matters for selectionen
dc.title.alternativeA generalized selection correction estimatoren
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

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