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A Zero Inflated Regression Model for Grouped Data


  • Sarah Brown

    (Department of Economics, University of Sheffield)

  • Alan S Duncan

    (Bankwest Curtin Economics Centre (BCEC), Curtin University)

  • Mark N Harris

    (School of Economics and Finance, Curtin University)

  • Jennifer Roberts

    (Department of Economics, University of Sheffield)

  • Karl Taylor

    (Department of Economics, University of Sheffield)


We introduce the (panel) zero-inflated interval regression (ZIIR) model, which is ideally suited when data are in the form of groups, which is commonly the case in survey data, and there is an ‘excess’ of zero observations. We apply our new modelling framework to the analysis of visits to general practitioners (GPs) using individual-level panel data from the British Household Panel Survey (BHPS). The ZIIR model simultaneously estimates the probability of visiting the GP and the frequency of visits (defined by given numerical intervals in the data). The results show that different socio-economic factors influence the probability of visiting the GP and the frequency of visits, thereby providing potentially valuable information to policy-makers concerned with health care allocation.

Suggested Citation

  • Sarah Brown & Alan S Duncan & Mark N Harris & Jennifer Roberts & Karl Taylor, 2014. "A Zero Inflated Regression Model for Grouped Data," Bankwest Curtin Economics Centre Working Paper series WP1401, Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
  • Handle: RePEc:ozl:bcecwp:wp1401

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    References listed on IDEAS

    1. Freund, Deborah A. & Kniesner, Thomas J. & LoSasso, Anthony T., 1999. "Dealing with the common econometric problems of count data with excess zeros, endogenous treatment effects, and attrition bias," Economics Letters, Elsevier, vol. 62(1), pages 7-12, January.
    2. Peter G. Moffatt & Simon A. Peters, 2000. "Grouped zero-inflated count data models of coital frequency," Journal of Population Economics, Springer;European Society for Population Economics, vol. 13(2), pages 205-220.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588.
    4. Jones, Andrew M, 1989. "A Double-Hurdle Model of Cigarette Consumption," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(1), pages 23-39, Jan.-Mar..
    5. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    6. Gurmu, Shiferaw & Elder, John, 2008. "A bivariate zero-inflated count data regression model with unrestricted correlation," Economics Letters, Elsevier, vol. 100(2), pages 245-248, August.
    7. Wang, Peiming, 2003. "A bivariate zero-inflated negative binomial regression model for count data with excess zeros," Economics Letters, Elsevier, vol. 78(3), pages 373-378, March.
    8. Harris, Mark N. & Zhao, Xueyan, 2007. "A zero-inflated ordered probit model, with an application to modelling tobacco consumption," Journal of Econometrics, Elsevier, vol. 141(2), pages 1073-1099, December.
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    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2020. "Using biomarkers to predict healthcare costs: Evidence from a UK household panel," Journal of Health Economics, Elsevier, vol. 73(C).
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    3. Cristian Roner & Claudia Di Caterina & Davide Ferrari, 2021. "Exponential Tilting for Zero-inflated Interval Regression with Applications to Cyber Security Survey Data," BEMPS - Bozen Economics & Management Paper Series BEMPS85, Faculty of Economics and Management at the Free University of Bozen.
    4. Gregory N. Price, 2019. "Does Productivity in the Formal Food Sector Drive Human Ebola Virus Infections in Sub‐Saharan Africa?," African Development Review, African Development Bank, vol. 31(2), pages 167-178, June.

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    More about this item


    interval regression; inflated responses; health care allocation; general practitioners;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D1 - Microeconomics - - Household Behavior
    • I1 - Health, Education, and Welfare - - Health

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