When outcome heterogeneously matters for selection

Simple item page
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.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.identifier.urihttp://hdl.handle.net/2003/29648
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5377
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

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
DP_4012_SFB823_Reichert_Tauchmann.pdf
Size:
206.42 KB
Format:
Adobe Portable Document Format
Description:
DNB
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.02 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Sonderforschungsbereich (SFB) 823