EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects

Zezhun Chen Chen, Angelos Dassios and George Tzougas

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

Abstract: This article considers bivariate mixed Poisson INAR(1) regression models with correlated random effects for modelling correlations of different signs and magnitude among time series of different types of claim counts. This is the first time that the proposed family of INAR(1) models is used in a statistical or actuarial context. For expository purposes, the bivariate mixed Poisson INAR(1) claim count regression models with correlated Lognormal and Gamma random effects paired via a Gaussian copula are presented as competitive alternatives to the classical bivariate Negative Binomial INAR(1) claim count regression model which only allows for positive dependence between the time series of claim count responses. Our main achievement is that we develop novel alternative Expectation-Maximization type algorithms for maximum likelihood estimation of the parameters of the models which are demonstrated to perform satisfactory when the models are fitted to Local Government Property Insurance Fund data from the state of Wisconsin.

Keywords: count data time series; Binomial-mixed Poisson INAR(1) regression models with correlated random effects; overdispersion; Gaussian copula; correlations of different signs and magnitude (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2024-04-30
New Economics Papers: this item is included in nep-ecm
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Published in European Actuarial Journal, 30, April, 2024, 14(1), pp. 225 – 255. ISSN: 2190-9733

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:118826

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