Quintana, José Mario (1987) Multivariate Bayesian forecasting models. PhD thesis, University of Warwick.

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Abstract

This thesis concerns theoretical and practical Bayesian modelling of multivariate time series. Our
main goal is to intruduce useful, flexible and tractable multivariate forecasting models and provide the
necessary theory for their practical implementation.
After a brief review of the dynamic linear model we formulate a new matrix-v-ariate generalization in
which a significant part of the variance-covariance structure is unknown. And a new general algorithm,
based on the sweep operator is provided for its recursive implementation. This enables important
advances to be made in long-standing problems related with the specification of the variances. We
address the problem of plug-in estimation and apply our results in the context of dynamic linear
models. We extend our matrix-variate model by considering the unknown part of the variance-covariance
structure to be dynamic. Furthermore, we formulate the dynamic recursive model which is a general
counterpart of fully recursive econometric models. The latter part of the dissertation is devoted to
modelling aspects. The usefulness of the methods proposed is illustrated with several examples involving
real and simulated data.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Multivariate analysis, Time-series analysis
Official Date:	March 1987
Dates:	Date Event March 1987 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	West, Mike, 1959-
Sponsors:	Consejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT)
Extent:	[6], 120 leaves
Language:	eng

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