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dc.contributor.authorKleppe, Tore Sellanden
dc.contributor.authorLiesenfeld, Romanen
dc.date.accessioned2011-12-14T09:56:45Z-
dc.date.available2011-12-14T09:56:45Z-
dc.date.issued2011-
dc.identifier.urihttp://hdl.handle.net/10419/53125-
dc.description.abstractThis paper provides high-dimensional and flexible importance sampling procedures for the likelihood evaluation of dynamic latent variable models involving finite or infinite mixtures leading to possibly heavy tailed and/or multi-modal target densities. Our approach is based upon the efficient importance sampling (EIS) approach of Richard and Zhang (2007) and exploits the mixture structure of the model when constructing importance sampling distributions as mixture of distributions. The proposed mixture EIS procedures are illustrated with ML estimation of a student-t state space model for realized volatilities and a stochastic volatility model with leverage effects and jumps for asset returns.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2011-11en
dc.subject.jelC15en
dc.subject.ddc330en
dc.subject.keyworddynamic latent variable modelen
dc.subject.keywordimportance samplingen
dc.subject.keywordmarginalized likelihooden
dc.subject.keywordmixtureen
dc.subject.keywordMonte Carloen
dc.subject.keywordrealized volatilityen
dc.subject.keywordstochastic volatilityen
dc.titleEfficient high-dimensional importance sampling in mixture frameworks-
dc.typeWorking Paperen
dc.identifier.ppn679408649en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:201111en

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