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dc.contributor.authorLoddo, Antonello, 1976-eng
dc.contributor.authorNi, Shawn, 1962-eng
dc.contributor.authorSun, Dongchueng
dc.description.abstractWe propose a Bayesian stochastic search approach to selecting restrictions on multivariate regression models where the errors exhibit deterministic or stochastic conditional volatilities. We develop a Markov Chain Monte Carlo (MCMC) algorithm that generates posterior restrictions on the regression coefficients and Cholesky decompositions of the covariance matrix of the errors. Numerical simulations with artificially generated data show that the proposed method is effective in selecting the data-generating model restrictions and improving the forecasting performance of the model. Applying the method to daily foreign exchange rate data, we conduct stochastic search on a VAR model with stochastic conditional volatilities.eng
dc.description.sponsorshipNi's research was supported by a grant from the MU Research Council and by Hong Kong Research Grant Council grant CUHK4128/03H. Sun's research was supported by the National Science Foundation grant SES-0720229, and NIH grants R01-MH071418 and R01-CA112159.eng
dc.identifier.citationDepartment of Economics, 2009eng
dc.publisherDepartment of Economicseng
dc.relation.ispartofEconomics publications (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Economicseng
dc.relation.ispartofseriesWorking papers (Department of Economics);WP 09-11eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subjectmodel selectioneng
dc.subjecteconomic modelseng
dc.subject.lcshEconomics -- Mathematical modelseng
dc.subject.lcshMarkov processeseng
dc.titleSelection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Searcheng
dc.typeWorking Papereng

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