Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/60668 
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dc.contributor.authorKoop, Gary M.en
dc.contributor.authorPotter, Simon M.en
dc.date.accessioned2012-08-17T14:28:30Z-
dc.date.available2012-08-17T14:28:30Z-
dc.date.issued2004-
dc.identifier.urihttp://hdl.handle.net/10419/60668-
dc.description.abstractThis paper discusses Bayesian inference in change-point models. Current approaches place a possibly hierarchical prior over a known number of change points. We show how two popular priors have some potentially undesirable properties, such as allocating excessive prior weight to change points near the end of the sample. We discuss how these properties relate to imposing a fixed number of change points in the sample. In our study, we develop a hierarchical approach that allows some change points to occur out of the sample. We show that this prior has desirable properties and handles cases with unknown change points. Our hierarchical approach can be shown to nest a wide variety of change-point models, from time-varying parameter models to those with few or no breaks. Data-based learning about the parameter that controls this variety occurs because our prior is hierarchical.en
dc.language.isoengen
dc.publisher|aFederal Reserve Bank of New York |cNew York, NYen
dc.relation.ispartofseries|aStaff Report |x197en
dc.subject.jelC11en
dc.subject.jelC22en
dc.subject.jelE17en
dc.subject.ddc330en
dc.subject.keywordBayesian, structural breaks, hierarchical prioren
dc.subject.stwBayes-Statistiken
dc.subject.stwStrukturbruchen
dc.subject.stwInferenzstatistiken
dc.titlePrior elicitation in multiple change-point models-
dc.typeWorking Paperen
dc.identifier.ppn481121374en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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