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Experience rating in non-life insurance

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  • Jean Pinquet

    (X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris)

Abstract

This paper presents statistical models which lead to experience rating in insurance. Serial correlation for risk variables can receive endogeneous or exogeneous explanations. The interpretation retained by actuarial models is exogeneous and reflects the positive contagion usually observed for the number of claims. This positive contagion can be explained by the revelation throughout time of a hidden features in the risk distributions. These features are represented by fixed effects which are predicted with a random effects model. This article discusses identification issues on the nature of the dynamics of non-life insurance data. Example of predictions are given for count data models with a constant or time-varying random effects, one or several equations, and for cost-number models on events.

Suggested Citation

  • Jean Pinquet, 2012. "Experience rating in non-life insurance," Working Papers hal-00677100, HAL.
    • Handle: RePEc:hal:wpaper:hal-00677100
    Note: View the original document on HAL open archive server: https://hal.science/hal-00677100
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    References listed on IDEAS

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    1. Angers, Jean-François & Desjardins, Denise & Dionne, Georges & Guertin, François, 2006. "Vehicle and Fleet Random Effects in a Model of Insurance Rating for Fleets of Vehicles," ASTIN Bulletin, Cambridge University Press, vol. 36(1), pages 25-77, May.
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    6. Desjardins, Denise & Dionne, Georges & Pinquet, Jean, 2001. "Experience Rating Schemes for Fleets of Vehicles," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 81-105, May.
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    13. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
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    15. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
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    Cited by:

    1. Dionne, Georges, 2012. "The empirical measure of information problems with emphasis on insurance fraud and dynamic data," Working Papers 12-10, HEC Montreal, Canada Research Chair in Risk Management.

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    Keywords

    Fixed and random effects; overdispersion; expected value principle; linear credibility approach.; linear credibility approach;
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