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Panel data methods and applications to health economics

  • Andrew M. Jones

Much of the empirical analysis done by health economists seeks to estimate the impact of specific health policies and the greatest challenge for successful applied work is to find appropriate sources of variation to identify the treatment effects of interest. Estimation can be prone to selection bias, when the assignment to treatments is associated with the potential outcomes of the treatment. Overcoming this bias requires variation in the assignment of treatments that is independent of the outcomes. One source of independent variation comes from randomised controlled experiments. But, in practice, most economic studies have to draw on non-experimental data. Many studies seek to use variation across time and events that takes the form of a quasi-experimental design, or “natural experiment”, that mimics the features of a genuine experiment. This chapter reviews the data and methods that are used in applied health economics with a particular emphasis on the use of panel data. The focus is on nonlinear models and methods that can accommodate unobserved heterogeneity. These include conditional estimators, maximum simulated likelihood, Bayesian MCMC, finite mixtures and copulas.

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Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 07/18.

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Date of creation: Aug 2007
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Handle: RePEc:yor:hectdg:07/18
Contact details of provider: Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
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