Queen, Catriona M. (1991) Bayesian graphical forecasting models for business time series. PhD thesis, University of Warwick.

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Abstract

This thesis develops three new classes of Bayesian graphical models to forecast
multivariate time series. Although these models were originally motivated by
the need for flexible and tractable forecasting models appropriate for modelling
competitive business markets, they are of theoretical interest in their own right.
Multiregression dynamic models are defined to preserve certain conditional
independence structures over time. Although these models are typically very
non-Gaussian, it is proved that they are simple to update, amenable to practical
implementation and promise more efficient identification of causal structures in
a time series than has been possible in the past.
Dynamic graphical models are defined for multivariate time series for which
there is believed to be symmetry between certain subsets of variables and a causal
driving mechanism between these subsets. They are a specific type of graphical
chain model (Wermuth & Lauritzen, 1990) which are once again typically non-
Gaussian. Dynamic graphical models are a combination of multiregression dynamic
models and multivariate regression models (Quintana, 1985,87, Quintana
& West, 1987,88) and as such, they inherit the simplicity of both these models.
Partial segmentation models extend the work of Dickey et al. (1987) to the
study of models with latent conditional independence structures. Conjugate
Bayesian anaylses are developed for processes whose probability parameters are
hypothesised to be dependent, using the fact that a certain likelihood separates
given a matrix of likelihood ratios. It is shown how these processes can be represented
by undirected graphs and how these help in its reparameterisation into
conjugate form.

Item Type:	Thesis (PhD)
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Time-series analysis, Multivariate analysis -- Graphic methods, Econometric models
Official Date:	September 1991
Dates:	Date Event September 1991 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Smith, J. Q., 1953-
Sponsors:	Science and Engineering Research Council (Great Britain) (SERC) ; Unilever Research
Extent:	ix, 171 leaves
Language:	eng

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