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“Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data”

Author

Listed:
  • Catalina Bolancé

    (Department of Econometrics, Riskcenter-IREA, University of Barcelona, Avinguda Diagonal 690, 08034 Barcelona, Spain.)

  • Ricardo Cao

    (Research Group MODES, Department of Mathematics, CITIC, Universidade da Coruña and ITMATI Campus de Elviña, s/n 15071 A Coruña, Spain.)

  • Montserrat Guillen

    (Department of Econometrics, Riskcenter-IREA, University of Barcelona, Avinguda Diagonal 690, 08034 Barcelona, Spain.)

Abstract

Estimation in single-index models for risk assessment is developed. Statistical properties are given and an application to estimate the cost of traffic accidents in an innovative insurance data set that has information on driving style is presented. A new kernel approach for the estimator covariance matrix is provided. Both, the simulation study and the real case show that the method provides the best results when data are highly skewed and when the conditional distribution is of interest. Supplementary materials containing appendices are available online.

Suggested Citation

  • Catalina Bolancé & Ricardo Cao & Montserrat Guillen, 2018. "“Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data”," IREA Working Papers 201829, University of Barcelona, Research Institute of Applied Economics, revised Dec 2018.
    • Handle: RePEc:ira:wpaper:201829
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    File URL: http://www.ub.edu/irea/working_papers/2018/201829.pdf
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    References listed on IDEAS

    as
    1. Hall, Peter & Yao, Qiwei, 2005. "Approximating conditional distribution functions using dimension reduction," LSE Research Online Documents on Economics 16333, London School of Economics and Political Science, LSE Library.
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    4. Hall, Peter & Wolff, Rodney C. L. & Yao, Qiwei, 1999. "Methods for estimating a conditional distribution function," LSE Research Online Documents on Economics 6631, London School of Economics and Political Science, LSE Library.
    5. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    6. Bashtannyk, David M. & Hyndman, Rob J., 2001. "Bandwidth selection for kernel conditional density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
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    8. Newey, Whitney K & Stoker, Thomas M, 1993. "Efficiency of Weighted Average Derivative Estimators and Index Models," Econometrica, Econometric Society, vol. 61(5), pages 1199-1223, September.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Insurance loss data; heavy tailed distributions; quantiles; non-parametric conditional distribution. JEL classification:C51; C14; G22;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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