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

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.

[1]  Pierre-André Chiappori,et al.  Testing for Asymmetric Information in Insurance Markets , 2000, Journal of Political Economy.

[2]  Bev Dahlby Testing for Asymmetric Information in Canadian Automobile Insurance , 1992 .

[3]  Georges Dionne,et al.  An Empirical Analysis of Moral Hazard and Experience Rating , 1989 .

[4]  A. Cameron,et al.  Econometric models based on count data. Comparisons and applications of some estimators and tests , 1986 .

[5]  Didier Richaudeau,et al.  Automobile Insurance Contracts and Risk of Accident: An Empirical Test Using French Individual Data , 1999 .

[6]  K. Yau,et al.  Zero‐Inflated Negative Binomial Mixed Regression Modeling of Over‐Dispersed Count Data with Extra Zeros , 2003 .

[7]  Bev Dahlby Adverse selection and statistical discrimination: An analysis of Canadian automobile insurance , 1983 .

[8]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[9]  J. Heckman,et al.  Adverse selection and moral hazard in insurance: Can dynamic data help to distinguish? , 2003 .

[10]  C. Gouriéroux,et al.  Evidence of Adverse Selection in Automobile Insurance Markets , 1998 .

[11]  Georges Dionne La mesure empirique des problemes d'information , 2009 .

[12]  Pierre-André Chiappori,et al.  Empirical contract theory: The case of insurance data , 1997 .

[13]  Q. Vuong Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[14]  Arthur Snow,et al.  Evidence on Adverse Selection: Equilibrium Signaling and Cross-Subsidization in the Insurance Market , 1994, Journal of Political Economy.

[15]  Pierre-André Chiappori,et al.  Asymmetric Information In Automobile Insurance: An Overview , 1999 .

[16]  R. Winkelmann Econometric Analysis of Count Data , 1997 .

[17]  Alma Cohen Asymmetric Information and Learning: Evidence from the Automobile Insurance Market , 2005 .