Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data
Catalina Bolancé (),
Ricarda Cao () and
Montserrat Guillen ()
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Catalina Bolancé: Department of Econometrics, Riskcenter-IREA, University of Barcelona, Avinguda Diagonal 690, 08034 Barcelona, Spain.
Ricarda 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.
No 201829, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics
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.
Keywords: Insurance loss data; heavy tailed distributions; quantiles; non-parametric conditional distribution. JEL classification:C51; C14; G22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-rmg
Date: 2018-12, Revised 2018-12
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Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:201829
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