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Forecasting—the Bayesian approach

Published online by Cambridge University Press:  20 April 2012

R. J. Verrall
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Extract

For most people, statistical forecasting means modelling time series using the methods described by Box and Jenkins (1970). A Box and Jenkins model requires a large number of known data points before it can be properly chosen and, since it cannot be changed without again observing another batch of input, is only of use if the data conforms in the future to the chosen model. This means that this style of forecasting is essentially rigid, unadaptable and of limited use in practice. This paper sets out, with the aid of the examples, the essentials and some applications of Bayesian forecasting as developed by Harrison and Stevens (1976).

Type
Research Article
Information
Journal of the Institute of Actuaries , Volume 110 , Issue 1 , June 1983 , pp. 183 - 203
Copyright
Copyright © Institute and Faculty of Actuaries 1983

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References

(1) Box, G. E. P. & Jenkins, G. M. (1970). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day.Google Scholar
(2) Harrison, P. J. & Stevens, C. F. (1976). Bayesian Forecasting (with Discussion), Journal of the Royal Statistical Society B 38, 205.Google Scholar
(3) Smith, J. Q. (1979). A Generalisation of the Bayesian Steady Forecasting Model. Journal of the Royal Statistical Society B 41, 375.Google Scholar