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    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Dissertations (MU)
    • 2006 Dissertations (MU)
    • 2006 MU dissertations - Freely available online
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    Bayesian analysis of multivariate stochastic volatility and dynamic models

    Loddo, Antonello, 1976-
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    Date
    2006
    Format
    Thesis
    Metadata
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    Abstract
    We 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.
    URI
    https://doi.org/10.32469/10355/4359
    https://hdl.handle.net/10355/4359
    Degree
    Ph. D.
    Thesis Department
    Statistics (MU)
    Rights
    OpenAccess.
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    • 2006 MU dissertations - Freely available online
    • Statistics electronic theses and dissertations (MU)

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