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Using simulation-based inference with panel data in health economics

  • Paul Contoyannis

    (Centre for Health Economics and Policy Analysis, McMaster University, UK)

  • Andrew M. Jones

    (Department of Economics and Related Studies, University of York, UK)

  • Roberto Leon-Gonzalez

    (Centre for Health Economics & Department of Economics and Related Studies, University of York, UK)

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). Copyright © 2003 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.811
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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 13 (2004)
Issue (Month): 2 ()
Pages: 101-122

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Handle: RePEc:wly:hlthec:v:13:y:2004:i:2:p:101-122
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. 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.
  2. 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.
  3. Chamberlain, Gary, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 225-38, January.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  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 761-64, May.
  9. V A Hajivassiliou, 1997. "Some Practical Issues in Maximum Simulated Likelihood," STICERD - Econometrics Paper Series /1997/340, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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