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dc.contributor.authorSchlicht, Ekkeharten
dc.contributor.authorLudsteck, Johannesen
dc.date.accessioned2006-09-27-
dc.date.accessioned2010-07-07T09:13:36Z-
dc.date.available2010-07-07T09:13:36Z-
dc.date.issued2006-
dc.identifier.urihttp://hdl.handle.net/10419/33643-
dc.description.abstractThis papers describes an estimator for a standard state-space model with coefficients generated by a random walk that is statistically superior to the Kalman filter as applied to this particular class of models. Two closely related estimators for the variances are introduced: A maximum likelihood estimator and a moments estimator that builds on the idea that some moments are equalized to their expectations. These estimators perform quite similar in many cases. In some cases, however, the moments estimator is preferable both to the proposed likelihood estimator and the Kalman filter, as implemented in the program package Eviews.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x2031en
dc.subject.jelC2en
dc.subject.jelC22en
dc.subject.jelC51en
dc.subject.jelC52en
dc.subject.ddc330en
dc.subject.keywordtime-varying coefficientsen
dc.subject.keywordadaptive estimationen
dc.subject.keywordKalman filteren
dc.subject.keywordstate-spaceen
dc.titleVariance estimation in a random coefficients model-
dc.type|aWorking Paperen
dc.identifier.ppn509753892en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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