Various mathematical and statistical models for estimation of automobile insurance pricing are reviewed. The methods are compared on their predictive ability based on two sets of automobile insurance data for two different states collected over two different periods. The issue of model complexity versus data availability is resolved through a comparison of the accuracy of prediction.The models reviewed range from the use of simple cell means to various multiplicative-additive schemes to the empirical-Bayes approach. The empirical-Bayes approach, with prediction based on both model-based and individual cell estimates, seems to yield the best forecast.

Additional Metadata
Keywords Additive-multiplicative-hybrid models, Automobile insurance, Empirical bayes, Predictive accuracy
Persistent URL dx.doi.org/10.1080/07350015.1984.10509385
Journal Journal of Business and Economic Statistics
Citation
Weisberg, H.I. (Herbert I.), Tomberlin, T.J, & Chatterjee, S. (Sangit). (1984). Predicting insurance losses under cross- classification: A comparison of alternative approaches. Journal of Business and Economic Statistics, 2(2), 170–178. doi:10.1080/07350015.1984.10509385