Using Simulation-based Inference with Panel Data in Health Economics

Paul Contoyannis (), Andrew Michael Jones () and Roberto Leon-Gonzalez ()

Department of Economics Working Papers from McMaster University

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).

New Economics Papers: this item is included in nep-cmp, nep-dcm and nep-hea
Date: 2002
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Related works:
Working Paper: Using Simulation-Based Inference with Panel Data in Health Economics
Journal Article: Using simulation-based inference with panel data in health economics (2004)
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Persistent link: http://EconPapers.repec.org/RePEc:mcm:deptwp:2002-13

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