Panel data methods and applications to health economics

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

21 Citations (Scopus)

Abstract

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 randomized 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.

Original languageEnglish
Title of host publicationPalgrave Handbook of Econometrics: Volume 2: Applied Econometrics
PublisherPalgrave Macmillan
Pages557-631
Number of pages75
ISBN (Electronic)9780230244405
ISBN (Print)9781403917997
DOIs
Publication statusPublished - 1 Jan 2009
Externally publishedYes

Cite this