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09-2

VIEWS: 12 PAGES: 1

									  Bayesian analysis of time series regressions with multivariate t
                       autoregressive errors

                               Tsung-I Lin (林宗儀)
             Department of Applied Mathematics and Institute of Statistcs,
                          National Chung Hsing University

                                      Abstract
We consider a Bayesian approach to the regression model with autoregressive
multivariate t errors in which the conditional variance satisfies a type of generalized
autoregressive conditionally heteroscedastic model. We present the approximate
Bayesian posterior and predictive inferences under a non-informative prior. Markov
chain Monte Carlo computational schemes are also developed for computing the
posterior uncertainties of parameters. To enhance the computational efficiency, we
provide a fast computation method of obtaining the inverse autocorrelation matrix of an
AR( p ) process. A real data example for the U.S. monthly Treasury constant maturity
rates is used to demonstrate our methodologies.

								
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