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Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution

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  • Lluís Bermúdez

    (Departament de Matemàtica Econòmica, Financera i Actuarial, Riskcenter-IREA, University of Barcelona, Av. Diagonal, 690, 08034 Barcelona, Spain
    These authors contributed equally to this work.)

  • Dimitris Karlis

    (Department of Statistics, Athens University of Economics and Business, 2, Troias, Kimolou & Spetson Str., 113 62 Athens, Greece
    These authors contributed equally to this work.)

Abstract

A multivariate INAR(1) regression model based on the Sarmanov distribution is proposed for modelling claim counts from an automobile insurance contract with different types of coverage. The correlation between claims from different coverage types is considered jointly with the serial correlation between the observations of the same policyholder observed over time. Several models based on the multivariate Sarmanov distribution are analyzed. The new models offer some advantages since they have all the advantages of the MINAR(1) regression model but allow for a more flexible dependence structure by using the Sarmanov distribution. Driven by a real panel data set, these models are considered and fitted to the data to discuss their goodness of fit and computational efficiency.

Suggested Citation

  • Lluís Bermúdez & Dimitris Karlis, 2021. "Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:505-:d:508246
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    References listed on IDEAS

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    Cited by:

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    2. Chen, Zezhun Chen & Dassios, Angelos & Tzougas, George, 2024. "EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects," LSE Research Online Documents on Economics 118826, London School of Economics and Political Science, LSE Library.

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