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dc.contributor.advisorSun, Dongchueng
dc.contributor.authorLoddo, Antonello, 1976-eng
dc.date.issued2006eng
dc.date.submitted2006 Summereng
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.eng
dc.descriptionTitle from title screen of research.pdf file viewed on (April 26, 2007)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph.D.) University of Missouri-Columbia 2006.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Statistics.eng
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.eng
dc.identifier.merlin.b5847089xeng
dc.identifier.oclc123570453eng
dc.identifier.otherLoddoA-070306-D5755eng
dc.identifier.urihttp://hdl.handle.net/10355/4359eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations.eng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshMarkov processeseng
dc.subject.lcshMonte Carlo methodeng
dc.titleBayesian analysis of multivariate stochastic volatility and dynamic modelseng
dc.typeThesiseng
thesis.degree.disciplineStatistics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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