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Using Simulation-based Inference with Panel Data in Health Economics

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  • Paul Contoyannis
  • Andrew M. Jones
  • Roberto Leon-Gonzalez

Abstract

Panel datasets provide a rich source of information for health economists, offering the scope to control for individual heterogeneity and to model the dynamics of individual behaviour. However the qualitative or categorical measures of outcome often used in health economics create special problems for estimating econometric models. Allowing a flexible specification of the autocorrelation induced by individual heterogeneity leads to models involving higher order integrals that cannot be handled by conventional numerical methods. The dramatic growth in computing power over recent years has been accompanied by the development of simulation-based estimators that solve this problem. This review uses binary choice models to show what can be done with conventional methods and how the range of models can be expanded by using simulation methods. Practical applications of the methods are illustrated using data on health from the British Household Panel Survey (BHPS).

Suggested Citation

  • Paul Contoyannis & Andrew M. Jones & Roberto Leon-Gonzalez, 2002. "Using Simulation-based Inference with Panel Data in Health Economics," Department of Economics Working Papers 2002-13, McMaster University.
  • Handle: RePEc:mcm:deptwp:2002-13
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    References listed on IDEAS

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    1. Robinson, Peter M, 1982. "On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables," Econometrica, Econometric Society, vol. 50(1), pages 27-41, January.
    2. Partha Deb, 2001. "A discrete random effects probit model with application to the demand for preventive care," Health Economics, John Wiley & Sons, Ltd., vol. 10(5), pages 371-383.
    3. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    4. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    5. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "Simulation-based inference in dynamic panel probit models: An application to health," Empirical Economics, Springer, vol. 29(1), pages 49-77, January.
    6. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    7. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    8. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-764, May.
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    Cited by:

    1. Timothy J. Halliday, 2008. "Heterogeneity, state dependence and health," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 499-516, November.
    2. Keane, Michael, 2004. "Modeling Health Insurance Choice Using the Heterogeneous Logit Model," MPRA Paper 55203, University Library of Munich, Germany.
    3. Shiko Maruyama, 2008. "Measuring the Welfare Effect of Entry in Differentiated Product Markets: The Case of Medicare HMOs," Discussion Papers 2008-01, School of Economics, The University of New South Wales.
    4. Shiko Maruyama, 2006. "Welfare Analysis Incorporating a Structural Entry-Exit Model: A Case Study of Medicare HMOs," Hi-Stat Discussion Paper Series d06-166, Institute of Economic Research, Hitotsubashi University.

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