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dc.contributor.authorWinker, Peteren
dc.contributor.authorMaringer, Dietmaren
dc.date.accessioned2009-01-30T12:09:36Z-
dc.date.available2009-01-30T12:09:36Z-
dc.date.issued2005-
dc.identifier.urihttp://hdl.handle.net/10419/23941-
dc.description.abstractThe convergence of estimators, e.g. maximum likelihood estimators, for increasing sample size is well understood in many cases. However, even when the rate of convergence of the estimator is known, practical application is hampered by the fact, that the estimator cannot always be obtained at tenable computational cost. This paper combines the analysis of convergence of the estimator itself with the analysis of the convergence of stochastic optimization algorithms, e.g. threshold accepting, to the theoretical estimator. We discuss the joint convergence of estimator and algorithm in a formal framework. An application to a GARCH-model demonstrates the approach in practice by estimating actual rates of convergence through a large scale simulation study. Despite of the additional stochastic component introduced by the use of an optimization heuristic, the overall quality of the estimates turns out to be superior compared to conventional approaches.en
dc.language.isoengen
dc.publisher|aUniversität Erfurt, Staatswissenschaftliche Fakultät |cErfurten
dc.relation.ispartofseries|aDiscussion Paper |x2005,004Een
dc.subject.jelC63en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordGARCHen
dc.subject.keywordThreshold Acceptingen
dc.subject.keywordOptimization Heuristicsen
dc.subject.keywordConvergenceen
dc.subject.stwSchätztheorieen
dc.subject.stwARCH-Modellen
dc.subject.stwTheorieen
dc.titleThe convergence of optimization based estimators : theory and application to a GARCH-model-
dc.typeWorking Paperen
dc.identifier.ppn504215086en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:erfdps:2005004Een

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