IDEAS home Printed from https://ideas.repec.org/a/ect/emjrnl/v7y2004i2p426-454.html
   My bibliography  Save this article

Semiparametric mixture models for multivariate count data, with application

Author

Listed:
  • Marco Alfò
  • Giovanni Trovato

Abstract

The analysis of overdispersed counts has been the focus of a wide range 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 behaviour of the proposed models in a series of empirical situations. Copyright Royal Economic Socciety 2004

Suggested Citation

  • Marco Alfò & Giovanni Trovato, 2004. "Semiparametric mixture models for multivariate count data, with application," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 426-454, December.
  • Handle: RePEc:ect:emjrnl:v:7:y:2004:i:2:p:426-454
    as

    Download full text from publisher

    File URL: http://www.blackwell-synergy.com/servlet/useragent?func=synergy&synergyAction=showTOC&journalCode=ectj&volume=7&issue=2&year=2004&part=null
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:pal:assmgt:v:18:y:2017:i:2:d:10.1057_s41260-016-0009-4 is not listed on IDEAS
    2. 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.
    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. Alka Chadha, 2005. "Trips and Patenting Activity: Evidence from the Indian Pharmaceutical Industry," Departmental Working Papers wp0512, National University of Singapore, Department of Economics.
    6. 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.
    7. 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.
    8. Leonardo Becchetti & Luisa Corrado & Fiammetta Rossetti, 2008. "Easterlin-types and Frustrated Achievers: the Heterogeneous E¤ects of Income Changes on Life Satisfaction," CEIS Research Paper 127, Tor Vergata University, CEIS, revised 09 Sep 2008.
    9. Tsung-Shan Tsou, 2016. "Robust likelihood inference for multivariate correlated count data," Computational Statistics, Springer, vol. 31(3), pages 845-857, September.
    10. 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.
    11. Chadha, Alka, 2009. "TRIPs and patenting activity: Evidence from the Indian pharmaceutical industry," Economic Modelling, Elsevier, vol. 26(2), pages 499-505, March.
    12. Alfò, Marco & Rocchetti, Irene, 2013. "A flexible approach to finite mixture regression models for multivariate mixed responses," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1754-1758.
    13. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ect:emjrnl:v:7:y:2004:i:2:p:426-454. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/resssea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.