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Use and Extension of Count Data Models in the Determination of Relevant Factors for Claims in the Automobile Insurance Sector

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  • Jose Antonio Ordaz
  • Maria del Carmen Melgar
  • M. Kazim Khan
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    Abstract

    Using real, Spanish data, different specifications of zero-inflated models are provided in this paper to estimate the number of accidents declared by policyholders. These count data models seem to be the most appropriate solutions to study this question. Our work is completed with the estimations of the number of clients that do not declare their actual accidents and the number of these accidents. The analysis of all these factors could become useful for insurers to improve their efficiency. We conclude with a final theoretical discussion on the possible advantages given by other alternative models, like the so-called thinned models.

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    File URL: http://www.ersj.eu/repec/ers/papers/10_4_p8.pdf
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    Bibliographic Info

    Article provided by European Research Studies Journal in its journal European Research Studies Journal.

    Volume (Year): XIII (2010)
    Issue (Month): 4 ()
    Pages: 119-138

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    Handle: RePEc:ers:journl:v:xiii:y:2010:i:4:p:119-138

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    Web page: http://www.ersj.eu/

    Related research

    Keywords: Accident; zero-inflated model; ‘extra-zeros’ estimation;

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    References

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    1. Dionne, G. & Gourieroux, C. & Vanasse, C., 1998. "Evidence of Adverse Selection in Automobile Insurance Markets," Ecole des Hautes Etudes Commerciales de Montreal- 98-09, Ecole des Hautes Etudes Commerciales de Montreal-Chaire de gestion des risques..
    2. Dionne, Georges, 1998. "La mesure empirique des problèmes d’information," L'Actualité Economique, Société Canadienne de Science Economique, vol. 74(4), pages 585-606, décembre.
    3. Alma Cohen, 2005. "Asymmetric Information and Learning: Evidence from the Automobile Insurance Market," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 197-207, May.
    4. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    5. Jaap H. Abbring & James J. Heckman & Pierre-André Chiappori & Jean Pinquet, 2003. "Adverse Selection and Moral Hazard In Insurance: Can Dynamic Data Help to Distinguish?," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 512-521, 04/05.
    6. Chiappori, Pierre-Andre & Salanie, Bernard, 1997. "Empirical contract theory: The case of insurance data," European Economic Review, Elsevier, vol. 41(3-5), pages 943-950, April.
    7. Didier Richaudeau, 1999. "Automobile Insurance Contracts and Risk of Accident: An Empirical Test Using French Individual Data," The Geneva Risk and Insurance Review, Palgrave Macmillan, vol. 24(1), pages 97-114, June.
    8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
    9. Puelz, Robert & Snow, Arthur, 1994. "Evidence on Adverse Selection: Equilibrium Signaling and Cross-Subsidization in the Insurance Market," Journal of Political Economy, University of Chicago Press, vol. 102(2), pages 236-57, April.
    10. Boyer, Marcel & Dionne, Georges, 1989. "An Empirical Analysis of Moral Hazard and Experience Rating," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 128-34, February.
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