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Count data models with selectivity

Author

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  • R. Winkelmann

Abstract

This paper shows how truncated, censored, hurdle, zero inflated and underreported count models can be interpreted as models with selectivity. Until recently, users of such count data models have commonly imposed independence brtween the count generating mechanism and the selection mechanism. Such an assumption is unrealistic in most applications, and various models with endogenous selectivity (correlation between the count and the selection equations) are presented. The methods are illustrated in an application to labor mobility where the dependent variable is the number of individual job changes during a ten year period.

Suggested Citation

  • R. Winkelmann, 1998. "Count data models with selectivity," Econometric Reviews, Taylor & Francis Journals, vol. 17(4), pages 339-359.
  • Handle: RePEc:taf:emetrv:v:17:y:1998:i:4:p:339-359
    DOI: 10.1080/07474939808800422
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    Citations

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    Cited by:

    1. Massimiliano Bratti & Alfonso Miranda, 2010. "Non‐pecuniary returns to higher education: the effect on smoking intensity in the UK," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 906-920, August.
    2. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    3. Munkin, Murat K., 2003. "The MCMC and SML estimation of a self-selection model with two outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 403-424, March.
    4. Sergi Jiménez-Martín & José M. Labeaga & Maite Martínez-Granado, 2002. "Latent class versus two-part models in the demand for physician services across the European Union," Health Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 301-321.
    5. Nicolas Sauter, 2012. "Talking trade: language barriers in intra-Canadian commerce," Empirical Economics, Springer, vol. 42(1), pages 301-323, February.
    6. Papadopoulos, Georgios & Santos Silva, Joao M C, 2008. "Identification issues in models for underreported counts," Economics Discussion Papers 3552, University of Essex, Department of Economics.
    7. Sergi Jiménez-Martín & José Labeaga & Maite Martínez-Granado, 2004. "An empirical analysis of the demand for physician services across the European Union," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 5(2), pages 150-165, May.
    8. Jochmans, Koen, 2015. "Multiplicative-error models with sample selection," Journal of Econometrics, Elsevier, vol. 184(2), pages 315-327.
    9. Bratti, M. & Miranda, A, 2010. "Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation," Health, Econometrics and Data Group (HEDG) Working Papers 10/19, HEDG, c/o Department of Economics, University of York.
    10. Engelstätter, Benjamin, 2009. "Enterprise systems and innovations," ZEW Discussion Papers 09-086, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    11. Munkin, Murat K. & Trivedi, Pravin K., 2003. "Bayesian analysis of a self-selection model with multiple outcomes using simulation-based estimation: an application to the demand for healthcare," Journal of Econometrics, Elsevier, vol. 114(2), pages 197-220, June.
    12. Vidhura Tennekoon, 2017. "Counting unreported abortions: A binomial-thinned zero-inflated Poisson model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(2), pages 41-72, January.
    13. Oya, Kosuke, 2005. "Properties of estimators of count data model with endogenous switching," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 536-544.
    14. James E. Prieger, "undated". "A Generalized Parametric Selection Model for Non-Normal Data," Department of Economics 00-09, California Davis - Department of Economics.
    15. Santos Silva, Joao M. C. & Windmeijer, Frank, 2001. "Two-part multiple spell models for health care demand," Journal of Econometrics, Elsevier, vol. 104(1), pages 67-89, August.
    16. Tom Van Ourti, 2004. "Measuring horizontal inequity in Belgian health care using a Gaussian random effects two part count data model," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 705-724.

    More about this item

    Keywords

    Poisson distribution; sample selection; underreporting; labor mobility; JEL Classification:C25; C42;

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods

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