Using Simulation-based Inference with Panel Data in Health Economics
AbstractPanel 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).
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Bibliographic InfoPaper provided by McMaster University in its series Department of Economics Working Papers with number 2002-13.
Length: 49 pages
Date of creation: 2002
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Other versions of this item:
- Paul Contoyannis & Andrew M. Jones & Roberto Leon-Gonzalez, 2004. "Using simulation-based inference with panel data in health economics," Health Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 101-122.
- Paul Contoyannis & Andrew M Jones & Roberto Leon-Gonzalez, . "Using Simulation-Based Inference with Panel Data in Health Economics," Discussion Papers 01/20, Department of Economics, University of York.
- NEP-ALL-2003-03-10 (All new papers)
- NEP-CMP-2003-03-10 (Computational Economics)
- NEP-DCM-2003-03-10 (Discrete Choice Models)
- NEP-HEA-2003-03-10 (Health Economics)
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