A Comparison of an Analytical Approach and a Standard Simulation Approach in Bayesian Forecasting Applied to Monthly Data from Insurance of Companies

Ingunn Fride Tvete () and Bent Natvig ()
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Ingunn Fride Tvete: Norwegian Computing Center
Bent Natvig: University of Oslo, and Norwegian Computing Center

Methodology and Computing in Applied Probability, 2002, vol. 4, issue 1, 95-113

Abstract: Abstract In this paper, we apply the theory of Bayesian forecasting and dynamic linear models, as presented in West and Harrison (1997), to monthly data from insurance of companies. The total number reported claims of compensation is chosen to be the primary time series of interest. The model is decomposed into a trend block, a seasonal effects block and a regression block with a transformed number of policies as regressor. An essential part of the West and Harrison (1997) approach is to find optimal discount factors for each block and hence avoid the specification of the variance matrices of the error terms in the system equations. The BATS package of Pole et al. (1994) is applied in the analysis. We compare predictions based on this analytical approach with predictions based on a standard simulation approach applying the BUGS package of Spiegelhalter et al. (1995). The motivation for this comparison is to gain knowledge on the quality of predictions based on more or less standard simulation techniques in other applications where an analytical approach is impossible. The predicted values of the two approaches are very similar. The uncertainties, however, in the predictions based on the simulation approach are far larger especially two months or more ahead. This partly indicates the advantages of applying optimal discount factors and partly the disadvantages of at least a standard simulation approach for long term predictions.

Keywords: dynamic linear models; Kalman filtering; block decomposition; optimal discount factors; Gibbs sampling (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1023/A:1015761615492

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