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dc.contributor.advisorSun, Dongchuen
dc.contributor.authorLoddo, Antonello, 1976-en_US
dc.date.issued2006eng
dc.date.submitted2006 Summeren
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.en_US
dc.descriptionTitle from title screen of research.pdf file viewed on (April 26, 2007)en_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionVita.en_US
dc.descriptionThesis (Ph.D.) University of Missouri-Columbia 2006.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Statistics.en_US
dc.description.abstractWe consider a multivariate regression model with time varying volatilities in the error term. The time varying volatility for each component of the error is of unknown nature, may be deterministic or stochastic. We propose Bayesian stochastic search as a feasible variable selection technique for the regression and volatility equations. We develop Markov Chain Monte Carlo (MCMC) algorithms that generate a posteriori restrictions on the elements of both the regression coefficients and the covariance matrix of the error term. Efficient parametrization of the time varying covariance matrices is studied using different modified Cholesky decompositions. We propose a hierarchal approach for selection of the volatility equation's variance components. We extend the results of the first in order to apply the stochastic search algorithm to dynamic model settings. We develop a MCMC algorithm that performs a stochastic model selection for the coefficients and the covariance matrix of the latent process of a dynamic model, thus making the choice of the best model only based on probabilistic considerations.en_US
dc.identifier.merlin.b5847089xen_US
dc.identifier.oclc123570453en_US
dc.identifier.otherLoddoA-070306-D5755en_US
dc.identifier.urihttp://hdl.handle.net/10355/4359
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartof2006 Freely available dissertations (MU)en_US
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2006 Dissertations
dc.subject.lcshBayesian statistical decision theoryen_US
dc.subject.lcshMarkov processesen_US
dc.subject.lcshMonte Carlo methoden_US
dc.titleBayesian analysis of multivariate stochastic volatility and dynamic modelsen_US
dc.typeThesisen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.disciplineStatisticseng
thesis.degree.grantorUniversity of Missouri--Columbiaen_US
thesis.degree.levelDoctoralen_US
thesis.degree.namePh.D.en_US


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