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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.

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Paper provided by Bankwest Curtin Economics Centre (BCEC), Curtin Business School in its series Bankwest Curtin Economics Centre Working Paper series with number WP1401.

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Length: 14 pages
Date of creation: Jan 2014
Date of revision:
Handle: RePEc:ozl:bcecwp:wp1401
Contact details of provider: Web page: http://business.curtin.edu.au/bcec
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  1. Jeffrey M. Wooldridge, 2001. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262232197, June.
  2. Peter G. Moffatt & Simon A. Peters, 2000. "Grouped zero-inflated count data models of coital frequency," Journal of Population Economics, Springer, vol. 13(2), pages 205-220.
  3. 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..
  4. 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.
  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. 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.
  7. 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.
  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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