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dc.contributor.authorKoop, Gary M.en_US
dc.contributor.authorPotter, Simon M.en_US
dc.date.accessioned2012-08-17T14:28:30Z-
dc.date.available2012-08-17T14:28:30Z-
dc.date.issued2004en_US
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_US
dc.language.isoengen_US
dc.publisherFederal Reserve Bank of New York New York, NYen_US
dc.relation.ispartofseriesStaff Report, Federal Reserve Bank of New York 197en_US
dc.subject.jelC11en_US
dc.subject.jelC22en_US
dc.subject.jelE17en_US
dc.subject.ddc330en_US
dc.subject.keywordBayesian, structural breaks, hierarchical prioren_US
dc.subject.stwBayes-Statistiken_US
dc.subject.stwStrukturbruchen_US
dc.subject.stwInferenzstatistiken_US
dc.titlePrior elicitation in multiple change-point modelsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn481121374en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Staff Reports, Federal Reserve Bank of New York

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