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The geometric combination of Bayesian forecasting models

Faria, Alvaro and Mubwandarikwa, Emmanuel (2008). The geometric combination of Bayesian forecasting models. Journal of Forecasting, 27(6) pp. 519–535.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1002/for.1071
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Abstract

A nonlinear geometric combination of statistical models is proposed as an alternative approach to the usual linear combination or mixture. Contrary to the linear, the geometric model is closed under the regular exponential family of distributions, as we show. As a consequence, the distribution which results from the combination is unimodal and a single location parameter can be chosen for decision making. In the case of Student t-distributions (of particular interest in forecasting) the geometric combination can be unimodal under a sufficient condition we have established. A comparative analysis between the geometric and linear combinations of predictive distributions from three Bayesian regression dynamic linear models, in a case of beer sales forecasting in Zimbabwe, shows the geometric model to consistently outperform its linear counterpart as well as its component models.

Item Type: Journal Article
ISSN: 0277-6693
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 12327
Depositing User: Àlvaro Faria
Date Deposited: 17 Nov 2008 13:37
Last Modified: 02 Dec 2010 20:15
URI: http://oro.open.ac.uk/id/eprint/12327
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