Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/23941
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dc.contributor.authorWinker, Peteren_US
dc.contributor.authorMaringer, Dietmaren_US
dc.date.accessioned2009-01-30T12:09:36Z-
dc.date.available2009-01-30T12:09:36Z-
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/10419/23941-
dc.description.abstractThe convergence of estimators, e.g. maximum likelihood estimators, forincreasing sample size is well understood in many cases. However, evenwhen the rate of convergence of the estimator is known, practical applicationis hampered by the fact, that the estimator cannot always be obtained attenable computational cost.This paper combines the analysis of convergence of the estimator itselfwith the analysis of the convergence of stochastic optimization algorithms,e.g. threshold accepting, to the theoretical estimator. We discuss the jointconvergence of estimator and algorithm in a formal framework.An application to a GARCH-model demonstrates the approach in practiceby estimating actual rates of convergence through a large scale simulationstudy. Despite of the additional stochastic component introduced by theuse of an optimization heuristic, the overall quality of the estimates turns outto be superior compared to conventional approaches.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseries|aDiscussion paper / Universität Erfurt, Staatswissenschaftliche Fakultät |x2005,004Een_US
dc.subject.jelC63en_US
dc.subject.jelC22en_US
dc.subject.ddc330en_US
dc.subject.keywordGARCHen_US
dc.subject.keywordThreshold Acceptingen_US
dc.subject.keywordOptimization Heuristicsen_US
dc.subject.keywordConvergenceen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwARCH-Modellen_US
dc.subject.stwTheorieen_US
dc.titleThe convergence of optimization based estimators : theory and application to a GARCH-modelen_US
dc.typeWorking Paperen_US
dc.identifier.ppn504215086en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:erfdps:2005004E-

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