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Semiparametric Mixture Models for Multivariate Count Data, with Application

Author

Listed:
  • Marco Alfò

    (Università degli Studi La Sapienza)

  • Giovanni Trovato

    (University of Rome II - Faculty of Economics)

Abstract
The analysis of overdispersed counts has been the focus of a large amount of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity or dependence among subjects. In this paper we extend the standard variance component models to the analysis of multivariate counts, defining the dependence among counts through a set of correlated random coefficients. Estimation is carried out by numerical integration through an EM algorithm without parametric assumptions upon the random coefficients distribution. The proposed model is computationally parsimonious and, when applied to a real dataset, seems to produce better results than parametric models. A simulation study has been carried out to investigate the behavior of the proposed models in a series of empirical situations.

Suggested Citation

  • Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    • Handle: RePEc:rtv:ceisrp:51
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    File URL: https://ceistorvergata.it/RePEc/rpaper/No-51-Alfo-Trovato.pdf
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    References listed on IDEAS

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    Cited by:

    1. Lim, Hwa Kyung & Li, Wai Keung & Yu, Philip L.H., 2014. "Zero-inflated Poisson regression mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 151-158.
    2. Alfò, Marco & Carbonari, Lorenzo & Trovato, Giovanni, 2023. "On the effects of taxation on growth: an empirical assessment," Macroeconomic Dynamics, Cambridge University Press, vol. 27(5), pages 1289-1318, July.
    3. Stefano Caiazza & Alberto Franco Pozzolo & Giovanni Trovato, 2016. "Bank efficiency measures, M&A decision and heterogeneity," Journal of Productivity Analysis, Springer, vol. 46(1), pages 25-41, August.
    4. Leonardo Becchetti & Roberto Rocci & Giovanni Trovato, 2007. "Industry and time specific deviations from fundamental values in a random coefficient model," Annals of Finance, Springer, vol. 3(2), pages 257-276, March.
    5. Leonardo Becchetti & Giovanni Trovato, 2011. "Corporate social responsibility and firm efficiency: a latent class stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(3), pages 231-246, December.
    6. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    7. Fabio Padovano & Nicolas Gavoille, 2013. "The Dual Political Legislation Cycle in France," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2013-02-ccr, Condorcet Center for political Economy, revised Jun 2014.
    8. Becchetti, L. & Corrado, L. & Rossetti , F., 2008. "Easterlin-types and Frustrated Achievers: the Heterogeneous Effects of Income Changes on Life Satisfaction," Cambridge Working Papers in Economics 0816, Faculty of Economics, University of Cambridge.
    9. Becchetti, Leonardo & Castriota, Stefano, 2010. "Wage differentials in social enterprises," AICCON Working Papers 68-2010, Associazione Italiana per la Cultura della Cooperazione e del Non Profit.
    10. Chadha, Alka, 2009. "TRIPs and patenting activity: Evidence from the Indian pharmaceutical industry," Economic Modelling, Elsevier, vol. 26(2), pages 499-505, March.
    11. Giuseppe Galloppo & Giovanni Trovato, 2017. "Fundamental driver of fund style drift," Journal of Asset Management, Palgrave Macmillan, vol. 18(2), pages 99-123, March.
    12. Landry, Craig E. & Liu, Haiyong, 2009. "A semi-parametric estimator for revealed and stated preference data--An application to recreational beach visitation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 205-218, March.

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    Keywords

    Correlated counts; Multivariate counts; Correlated random effects; Non-parametric ML;
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