Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach

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dc.contributor.authorRios-Avila, Fernando
dc.contributor.authorCanavire-Bacarreza, Gustavo
dc.contributor.eafitauthorgcanavir@eafit.edu.co
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.date.accessioned2017-05-23T20:11:03Z
dc.date.available2017-05-23T20:11:03Z
dc.date.issued2017-05-01
dc.description.abstractFollowing Wooldridge (2014), we discuss and implement in Stata an efficient maximum likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of this estimate using different non-linear routines already implemented in Stata such as ivprobit, ivtobit, ivpoisson, heckman, and ivregress.eng
dc.identifier.urihttp://hdl.handle.net/10784/11432
dc.language.isoengeng
dc.publisherUniversidad EAFITspa
dc.publisher.departmentEscuela de Economía y Finanzasspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subject.keywordMaximum Likelihood Estimationspa
dc.subject.keywordnon-linear modelsspa
dc.subject.keywordendogeneityspa
dc.subject.keywordtwo-step modelsspa
dc.subject.keywordstandard errorsspa
dc.titleStandard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approacheng
dc.typeworkingPapereng
dc.typeinfo:eu-repo/semantics/workingPapereng
dc.type.hasVersiondrafteng
dc.type.localDocumento de trabajo de investigaciónspa

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