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Bivariate generalized Poisson regression model: applications on health care data

Author

Listed:
  • Hossein Zamani

    (Hormozgan University)

  • Pouya Faroughi

    (Islamic Azad University)

  • Noriszura Ismail

    (Universiti Kebangsaan Malaysia (UKM))

Abstract

This paper introduces several forms of bivariate generalized Poisson regression model (BGPR) which can be fitted to bivariate and correlated count data with covariates. The main advantage of these forms of BGPR is that they are nested and thus they allow likelihood ratio tests to be performed to choose the best model. The BGPR can be fitted not only to bivariate count data with positive, zero, or negative correlations, but also to under- or overdispersed bivariate count data with flexible form of mean–variance relationship. Applications of several forms of the BGPR are illustrated on two sets of count data: the Australian health survey data and the US National Medical Expenditure Survey data.

Suggested Citation

  • Hossein Zamani & Pouya Faroughi & Noriszura Ismail, 2016. "Bivariate generalized Poisson regression model: applications on health care data," Empirical Economics, Springer, vol. 51(4), pages 1607-1621, December.
    • Handle: RePEc:spr:empeco:v:51:y:2016:i:4:d:10.1007_s00181-015-1051-7
    DOI: 10.1007/s00181-015-1051-7
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    References listed on IDEAS

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    1. Gurmu, Shiferaw & Elder, John, 2000. "Generalized bivariate count data regression models," Economics Letters, Elsevier, vol. 68(1), pages 31-36, July.
    2. A. C. Cameron & P. K. Trivedi & Frank Milne & J. Piggott, 1988. "A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 55(1), pages 85-106.
    3. Felix Famoye & P. Consul, 1995. "Bivariate generalized Poisson distribution with some applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 127-138, December.
    4. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-223, May-June.
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    6. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
    7. Hossein Zamani & Noriszura Ismail, 2013. "Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2056-2068, September.
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    Citations

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    Cited by:

    1. Najla Qarmalah & Abdulhamid A. Alzaid, 2023. "Zero-Dependent Bivariate Poisson Distribution with Applications," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    2. Purhadi & Sutikno & Sarni Maniar Berliana & Dewi Indra Setiawan, 2021. "Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data," Letters in Spatial and Resource Sciences, Springer, vol. 14(1), pages 79-99, April.
    3. Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
    4. Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    5. Lluís Bermúdez & Dimitris Karlis, 2022. "Copula-based bivariate finite mixture regression models with an application for insurance claim count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1082-1099, December.

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    More about this item

    Keywords

    Generalized Poisson; Bivariate; Correlation; Overdispersion; Underdispersion;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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