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What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

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  • Markus Jochmann

    ()
    ( Department of Economics, University of Strathclyde)

Abstract

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 45_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:45_09

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Keywords: Bayesian; model selection; model averaging; count data; zero-inflation; demand for health care;

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  1. Deb, Partha & Munkin, Murat K. & Trivedi, Pravin K., 2006. "Private Insurance, Selection, and Health Care Use: A Bayesian Analysis of a Roy-Type Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 403-415, October.
  2. Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.
  3. 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.
  4. 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.
  5. Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
  6. Dirk Eddelbuettel & Romain Francois, . "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, American Statistical Association, vol. 40(i08).
  7. 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.
  8. Nazmi Sari, 2009. "Physical inactivity and its impact on healthcare utilization," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 885-901.
  9. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
  10. D. Böhning & E. Dietz & P. Schlattmann & L. Mendonça & U. Kirchner, 1999. "The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 195-209.
  11. Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
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