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dc.contributor.authorSchlicht, Ekkeharten_US
dc.contributor.authorLudsteck, Johannesen_US
dc.date.accessioned2006-09-27en_US
dc.date.accessioned2010-07-07T09:13:36Z-
dc.date.available2010-07-07T09:13:36Z-
dc.date.issued2006en_US
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_US
dc.language.isoengen_US
dc.publisherIZA Bonnen_US
dc.relation.ispartofseriesIZA Discussion Papers 2031en_US
dc.subject.jelC2en_US
dc.subject.jelC22en_US
dc.subject.jelC51en_US
dc.subject.jelC52en_US
dc.subject.ddc330en_US
dc.subject.keywordtime-varying coefficientsen_US
dc.subject.keywordadaptive estimationen_US
dc.subject.keywordKalman filteren_US
dc.subject.keywordstate-spaceen_US
dc.titleVariance estimation in a random coefficients modelen_US
dc.typeWorking Paperen_US
dc.identifier.ppn509753892en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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