IDEAS home Printed from https://ideas.repec.org/a/bxr/bxrceb/y2006v49i2p169-183.html
   My bibliography  Save this article

Une étude économétrique du nombre d'accidents dans le secteur de l'assurance automobile

Author

Listed:
  • María Del Carmen Melgar
  • José Antonio Ordaz
  • Flor María Guerrero

Abstract

The estimation of the number of accidents is one of the most important purposes in the research field of the auto insurance industry. Count data econometric models are usually employed in this process, in particular the traditional Poisson and the negative binomial specifications. Nevertheless, zero-inflated models could be more appropriated solutions. The main objective of this paper is to show the most significant factors in the accidents that are declared by the policyholders. We estimate its number using the data provided by a Spanish private insurance company throughout the different models we have previously pointed out, comparing the obtained results.

Suggested Citation

  • María Del Carmen Melgar & José Antonio Ordaz & Flor María Guerrero, 2006. "Une étude économétrique du nombre d'accidents dans le secteur de l'assurance automobile," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 49(2), pages 169-183.
    • Handle: RePEc:bxr:bxrceb:y:2006:v:49:i:2:p:169-183
    as

    Download full text from publisher

    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/13222/1/ber-1105.pdf
    File Function: ber-1105
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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 236-257, April.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. Pierre-Andre Chiappori & Bernard Salanie, 2000. "Testing for Asymmetric Information in Insurance Markets," Journal of Political Economy, University of Chicago Press, vol. 108(1), pages 56-78, February.
    5. A. Colin Cameron & Pravin K. Trivedi, 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.
    6. 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.
    7. Mullahy, John, 1997. "Heterogeneity, Excess Zeros, and the Structure of Count Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 337-350, May-June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jose Antonio Ordaz & Maria del Carmen Melgar & M. Kazim Khan, 2010. "Use and Extension of Count Data Models in the Determination of Relevant Factors for Claims in the Automobile Insurance Sector," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 119-138.
    2. Maria del Carmen Melgar & Jose Antonio Ordaz, 2010. "The Utility Of Zero-Inflated Models In The Estimation Of The Number Of Accidents In The Automobile Insurance Industry," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 5(2), pages 181-194, December.
    3. Hyojoung Kim & Doyoung Kim & Subin Im & James W. Hardin, 2009. "Evidence of Asymmetric Information in the Automobile Insurance Market: Dichotomous Versus Multinomial Measurement of Insurance Coverage," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(2), pages 343-366, June.
    4. Pierre-André Chiappori & Bernard Salanié, 2002. "Testing Contract Theory : A Survey of Some Recent Work," Working Papers 2002-11, Center for Research in Economics and Statistics.
    5. Alma Cohen & Peter Siegelman, 2010. "Testing for Adverse Selection in Insurance Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(1), pages 39-84, March.
    6. GÓMEZ GARCÍA, J.Mª & PELÁEZ FERMOSO, F.J. & y GARCÍA GONZÁLEZ, A., 2005. "Repercusiones del envejecimiento demográfico sobre el sistema público de pensiones en Castilla y León," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 23, pages 235-253, Abril.
    7. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    8. Dahchour, Maki & Dionne, Georges, 2002. "Pricing of Automobile Insurance Under Asymmetric Information: a Study on Panel Data," Working Papers 01-6, HEC Montreal, Canada Research Chair in Risk Management.
    9. Jean Pinquet & Georges Dionne & Charles Vanasse & Mathieu Maurice, 2007. "Point-record incentives, asymmetric information and dynamic data," Working Papers hal-00243056, HAL.
    10. repec:mea:meawpa:12259 is not listed on IDEAS
    11. Hanming Fang & Michael P. Keane & Dan Silverman, 2008. "Sources of Advantageous Selection: Evidence from the Medigap Insurance Market," Journal of Political Economy, University of Chicago Press, vol. 116(2), pages 303-350, April.
    12. Georges Dionne & Pierre-Carl Michaud & Maki Dahchour, 2013. "Separating Moral Hazard From Adverse Selection And Learning In Automobile Insurance: Longitudinal Evidence From France," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 897-917, August.
    13. Hanming Fang & Zenan Wu, 2018. "Multidimensional private information, market structure, and insurance markets," RAND Journal of Economics, RAND Corporation, vol. 49(3), pages 751-787, September.
    14. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    15. Alois Geyer & Daniela Kremslehner & Alexander Muermann, 2020. "Asymmetric Information in Automobile Insurance: Evidence From Driving Behavior," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(4), pages 969-995, December.
    16. Gourieroux, C. & Jasiak, J., 2004. "Heterogeneous INAR(1) model with application to car insurance," Insurance: Mathematics and Economics, Elsevier, vol. 34(2), pages 177-192, April.
    17. Ciprian MatiÅŸ & Eugenia MatiÅŸ, 2013. "Asymmetric Information In Insurance Field: Some General Considerations," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(15), pages 1-17.
    18. Karl Ove Aarbu, 2017. "Asymmetric Information in the Home Insurance Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(1), pages 35-72, March.
    19. Huang, Rachel J. & Jeng, Vivian & Wang, Cheng-Wei & Yue, Jack C., 2021. "Does size and book-to-market contain intangible information about managerial incentives? Learning from corporate D&O insurance purchase," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    20. Martin Spindler & Joachim Winter & Steffen Hagmayer, 2014. "Asymmetric Information in the Market for Automobile Insurance: Evidence From Germany," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(4), pages 781-801, December.
    21. Dionne, Georges & Michaud, Pierre-Carl & Pinquet, Jean, 2013. "A review of recent theoretical and empirical analyses of asymmetric information in road safety and automobile insurance," Research in Transportation Economics, Elsevier, vol. 43(1), pages 85-97.

    More about this item

    Keywords

    accident; assurance automobile; modèles économétriques pour données de comptage; accident; automobile insurance; count data econometric models;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bxr:bxrceb:y:2006:v:49:i:2:p:169-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Benoit Pauwels (email available below). General contact details of provider: https://edirc.repec.org/data/dulbebe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.