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A dynamic hurdle model for zero-inflated count data: with an application to health care utilization

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  • Gregori Baetschmann
  • Rainer Winkelmann

Abstract

Excess zeros are encountered in many empirical count data applications. We provide a new explanation of extra zeros, related to the underlying stochastic process that generates events. The process has two rates, a lower rate until the first event, and a higher one thereafter. We derive the corresponding distribution of the number of events during a fixed period and extend it to account for observed and unobserved heterogeneity. An application to the socio-economic determinants of the individual number of doctor visits in Germany illustrates the usefulness of the new approach.

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Bibliographic Info

Paper provided by Department of Economics - University of Zurich in its series ECON - Working Papers with number 151.

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Date of creation: Apr 2014
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Handle: RePEc:zur:econwp:151

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Keywords: Excess zeros; Poisson process; exposure; hurdle model;

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  1. Street, Andrew & Jones, Andrew & Furuta, Aya, 1999. "Cost-sharing and pharmaceutical utilisation and expenditure in Russia," Journal of Health Economics, Elsevier, vol. 18(4), pages 459-472, August.
  2. Michele Campolieti, 2002. "The recurrence of occupational injuries: estimates from a zero inflated count model," Applied Economics Letters, Taylor & Francis Journals, vol. 9(9), pages 595-600.
  3. Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP) – Scope, Evolution and Enhancements," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(1), pages 139-169.
  4. Schmitz, Hendrik, 2013. "Practice budgets and the patient mix of physicians – The effect of a remuneration system reform on health care utilisation," Journal of Health Economics, Elsevier, vol. 32(6), pages 1240-1249.
  5. Heckman, James J & Borjas, George J, 1980. "Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence," Economica, London School of Economics and Political Science, vol. 47(187), pages 247-83, August.
  6. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
  7. Winkelmann, Rainer, 1995. "Duration Dependence and Dispersion in Count-Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 467-74, October.
  8. Pizer, Steven D. & Prentice, Julia C., 2011. "Time is money: Outpatient waiting times and health insurance choices of elderly veterans in the United States," Journal of Health Economics, Elsevier, vol. 30(4), pages 626-636, July.
  9. Nazmi Sari, 2009. "Physical inactivity and its impact on healthcare utilization," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 885-901.
  10. Helmut Farbmacher & Joachim Winter, 2013. "Per‐Period Co‐Payments And The Demand For Health Care: Evidence From Survey And Claims Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1111-1123, 09.
  11. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  12. McShane, Blake & Adrian, Moshe & Bradlow, Eric T & Fader, Peter S, 2008. "Count Models Based on Weibull Interarrival Times," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 369-378.
  13. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
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