Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/17331
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dc.contributor.authorSriram, Karthik
dc.contributor.authorShi, Peng
dc.contributor.authorGhosh, Pulak
dc.date.accessioned2016-01-10T09:14:24Z
dc.date.available2016-01-10T09:14:24Z
dc.date.copyright2015
dc.date.issued2015
dc.identifier.citationSriram, K., Shi, P. and Ghosh, P. (2016), A Bayesian quantile regression model for insurance company costs data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179: 177–202.en_US
dc.identifier.issn1467-985X
dc.identifier.urihttp://hdl.handle.net/11718/17331
dc.description.abstractWe examine the average cost function for property and casualty insurers. The cost function describes the relationship between a firm's minimum production cost and outputs. A comparison of cost functions could shed light on the relative cost efficiency of individual firms, which is of interest to many market participants and has been given extensive attention in the insurance industry. To identify and to compare the cost function, current practice is to assume a common functional form between costs and outputs across insurers and then to rank insurers according to the centre of the cost distribution. However, the assumption of a common cost–output relationship could be misleading because insurers tend to adopt different technologies that are reflected by the cost function in their production process. The centre-based comparison could also lead to biased inference especially when the cost distribution is skewed with a heavy tail. To address these issues, we model the average production cost of insurers by using a Bayesian quantile regression approach. Quantile regression enables the modelling of different quantiles of the cost distribution as opposed to just the centre. The Bayesian approach helps to estimate the cost-to-output functional relationship at a firm level by borrowing information across firms. In the analysis of US property–casualty insurers, we show that better insights into efficiency are gained by comparing different quantiles of the cost distribution.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.subjectAsymmetric Laplace distributionen_US
dc.subjectCost functionen_US
dc.subjectQuantile regressionen_US
dc.subjectSingle-index modelen_US
dc.titleA Bayesian quantile regression model for insurance company costs dataen_US
dc.typeArticleen_US
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