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Semiparametric Mixture Models for Multivariate Count Data, with Application

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Author Info
Giovanni Trovato () (University of Rome II - Faculty of Economics)
Marco Alf˜ () (Universitˆ degli Studi La Sapienza)
Vincenzo Atella () (University of Rome II - Faculty of Economics)

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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.

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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 51.

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Handle: RePEc:rtv:ceisrp:51

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Keywords: Correlated counts; Multivariate counts; Correlated random effects; Non-parametric ML;

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  3. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-35, October.
  4. Brannas, Kurt & Rosenqvist, Gunnar, 1994. "Semiparametric estimation of heterogeneous count data models," European Journal of Operational Research, Elsevier, vol. 76(2), pages 247-258, July. [Downloadable!] (restricted)
  5. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January. [Downloadable!] (restricted)
  6. Davies, Richard B., 1993. "Nonparametric control for residual heterogeneity in modelling recurrent behaviour," Computational Statistics & Data Analysis, Elsevier, vol. 16(2), pages 143-160, August. [Downloadable!] (restricted)
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  9. Cameron, A Colin & Trivedi, Pravin K, 1993. "Tests of Independence in Parametric Models with Applications and Illustrations," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 29-43, January.
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  10. van Ophem, Hans, 1999. "A General Method To Estimate Correlated Discrete Random Variables," Econometric Theory, Cambridge University Press, vol. 15(02), pages 228-237, April. [Downloadable!]
  11. 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 203-23, May-June. [Downloadable!]
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  14. Gurmu, Shiferaw & Elder, John, 2000. "Generalized bivariate count data regression models," Economics Letters, Elsevier, vol. 68(1), pages 31-36, July. [Downloadable!] (restricted)
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  1. 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. [Downloadable!] (restricted)
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  2. 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. [Downloadable!]
    Other versions:
  3. Alka Chadha, 2005. "Trips and Patenting Activity: Evidence from the Indian Pharmaceutical Industry," Departmental Working Papers wp0512, National University of Singapore, Department of Economics. [Downloadable!]
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