EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking

George Tzougas

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters.

Keywords: poisson-inverse gamma distribution; em algorithm; regression models for mean and dispersion parameters; motor third party liability insurance; ratemaking (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2020-09-11
New Economics Papers: this item is included in nep-ecm and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Risks, 11, September, 2020, 8(3), pp. 1-23. ISSN: 2227-9091

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:106539

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