EconStor >
Christian-Albrechts-Universität zu Kiel (CAU) >
Department of Economics, Universität Kiel  >
Economics Working Papers, Department of Economics, CAU Kiel >

Please use this identifier to cite or link to this item:

Full metadata record

DC FieldValueLanguage
dc.contributor.authorKleppe, Tore Sellanden_US
dc.contributor.authorLiesenfeld, Romanen_US
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_US
dc.publisherUniv., Dep. of Economics Kielen_US
dc.relation.ispartofseriesEconomics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2011,11en_US
dc.subject.keyworddynamic latent variable modelen_US
dc.subject.keywordimportance samplingen_US
dc.subject.keywordmarginalized likelihooden_US
dc.subject.keywordMonte Carloen_US
dc.subject.keywordrealized volatilityen_US
dc.subject.keywordstochastic volatilityen_US
dc.titleEfficient high-dimensional importance sampling in mixture frameworksen_US
dc.typeWorking Paperen_US
Appears in Collections:Economics Working Papers, Department of Economics, CAU Kiel

Files in This Item:
File Description SizeFormat
679408649.pdf1.83 MBAdobe PDF
No. of Downloads: Counter Stats
Show simple item record
Download bibliographical data as: BibTeX

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.