Cantarelis, Nick S. (1979) An investigation into the properties of Bayesian forecasting models. PhD thesis, University of Warwick.

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Abstract

In the early 70's, Harrison and Stevens
made a major contribution to the area of statistical forecasting.
They adopted a Bayesian approach in conjunction with a fundamental
model, first used by Kalman and called the Dynamic Linear
Model (DLM).
This thesis is concerned with the investigation of
the properties of different Bayesian forecasting models and
in particular of the Multi State Model (MSM). The latter is
important in postulating that no single DLM can adequately describe a
process with discontinuities and consequently the system defines a
number of models characterising the most likely process states.
A small number of parameters are shown to govern the behaviour of
the MSM and the relationship between the choice of these parameters.
and performance has been examined. It is shown that for a process
exhibiting discontinuities, traditional forecasting criteria such
as the mean square error are no longer appropriate. An alternative
set of performance measures is proposed and used as the main language
of understanding the variety of responses of the MSM to different types
and sizes of discontinuities. The parameter representing the noise
variance of the process is shown to be critical to the performance
of both the MSIl and other single state Bayesian models. A number
of on line variance estimation methods are proposed and tested on
artificial and real data. The methods are shown to be robust and
lead to improved performance not only of the MSM but the other
Bayesian single state models which of course require a noise variance
estimate.
Finally, alternative formulations of the MSM are proposed,
leading to significant reduction in the computational and storage
requirements while at the same time improving the response of the
MSM.

Item Type:	Thesis (PhD)
Subjects:	H Social Sciences > HB Economic Theory
Library of Congress Subject Headings (LCSH):	Economic forecasting, Bayesian statistical decision theory
Official Date:	December 1979
Dates:	Date Event December 1979 Submitted
Institution:	University of Warwick
Theses Department:	Warwick Business School
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Johnston, F. R.
Sponsors:	CIEBR ; Umbrako ; SIBS
Extent:	xi, 340, 108 leaves
Language:	eng

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