Worst-case estimation and asymptotic theory for models with unobservables

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dc.contributor.author Esteban-Bravo, Mercedes
dc.contributor.author Vidal-Sanz, Jose M.
dc.date.accessioned 2006-11-07T11:18:06Z
dc.date.available 2006-11-07T11:18:06Z
dc.date.issued 2004-11
dc.identifier.uri http://hdl.handle.net/10016/102
dc.description.abstract This paper proposes a worst-case approach for estimating econometric models containing unobservable variables. Worst-case estimators are robust against the adverse effects of unobservables. In contrast to the classical literature, there are no assumptions about the statistical nature of the unobservables in a worst-case estimation. This method is robust with respect to the unknown probability distribution of the unobservables and should be seen as a complement to standard methods, as cautious modelers should compare different estimations to determine robust models. The limit theory is obtained. A Monte Carlo study of finite sample properties has been conducted. An economic application is included.
dc.format.extent 598761 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries Workings Paper. Bussiness Economics
dc.relation.ispartofseries 2004-18
dc.title Worst-case estimation and asymptotic theory for models with unobservables
dc.type workingPaper
dc.subject.eciencia Empresa
dc.rights.accessRights openAccess
dc.identifier.repec wb045518
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