Experience Rating through Heterogeneous Models

This paper presents statistical models which lead to experience rating in insurance. Serial correlation for risk variables can receive endogeneous or exogeneous explanations. The paper recalls that the main interpretation for automobile insurance is exogeneous, since positive contagion is always observed for the number of claims reported and since true contagion should be negative. This positive contagion can be explained by the revelation throughout time of a hidden features in the risk distributions. These features are represented by heterogeneity components in a heterogeneous model. Prediction on longitudinal data can be performed through the heterogeneous model, and the paper provides consistent estimators for models related to number and cost of claims. Examples are given for count data models with a constant or time-varying heterogeneity components, one or several equations, and for a cost-number model on events. Empirical results are presented, which are drawn from the analysis of a French data base of automobile insurance contracts.

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