Health Econometrics Using Stata

Partha Deb is a professor of economics at Hunter College, City University of New York, where he teaches econometrics at the undergraduate and graduate levels. He has also taught, like his coauthors of this book, short courses in econometrics in the US, Europe, and Australia. He has developed a number of modeling packages in Stata. His research spans a range of topics that involve development and application of novel econometric methods in health economics, including modeling healthcare expenditures and use. Edward C. Norton is an economist at the University of Michigan, where he is a professor in the Department of Health Management and Policy and in the Department of Economics. His research interests in health economics include long-term care and aging, obesity, pay for performance, and applied econometrics. He is an associate editor for both Health Economics and the Journal of Health Economics. He has taught econometrics at the doctoral and master’s levels, making extensive use of Stata, for many years. Willard G. Manning was a professor at the Harris School and Health Studies at the University of Chicago at the time of his death in 2014. He had previously held positions at the University of Minnesota, University of Michigan, UCLA, RAND Corporation, and Harvard. He was well known as a champion of developing and applying rigorous econometric methods in health economics during his long career.

• Preface 1 Introduction 1.1 Outline 1.2 Themes 1.3 Health econometric myths 1.4 Stata friendly 1.5 A useful way forward 2 Framework 2.1 Introduction 2.2 Potential outcomes and treatment effects 2.3 Estimating ATEs 2.4 Regression estimates of treatment effects 2.5 Incremental and marginal effects 2.6 Model selection 2.7 Other issues 3 MEPS data 3.1 Introduction 3.2 Overview of all variables 3.3 Expenditure and use variables 3.4 Explanatory variables 3.5 Sample dataset 3.6 Stata resources 4 The linear regression model: Specification and checks 4.1 Introduction 4.2 The linear regression model 4.3 Marginal, incremental, and treatment effects 4.4 Consequences of misspecification 4.5 Visual checks 4.6 Statistical tests 4.7 Stata resources 5 Generalized linear models 5.1 Introduction 5.2 GLM framework 5.3 GLM examples 5.4 GLM predictions 5.5 GLM example with interaction term 5.6 Marginal and incremental effects 5.7 Example of marginal and incremental effects 5.8 Choice of link function and distribution family 5.9 Conclusions 5.10 Stata resources 6 Log and Box–Cox models 6.1 Introduction 6.2 Log models 6.3 Retransformation from ln(y) to raw scale 6.4 Comparison of log models to GLM 6.5 Box–Cox models 6.6 Stata resources 7 Models for continuous outcomes with mass at zero 7.1 Introduction 7.2 Two-part models 7.3 Generalized tobit 7.4 Comparison of two-part and generalized tobit models 7.5 Interpretation and marginal effects 7.6 Single-index models that accommodate zeros 7.7 Statistical tests 7.8 Stata resources 8 Count models 8.1 Introduction 8.2 Poisson regression 8.3 Negative binomial models 8.4 Hurdle and zero-inflated count models 8.5 Truncation and censoring 8.6 Model comparisons 8.7 Conclusion 8.8 Stata resources 9 Models for heterogeneous effects 9.1 Introduction 9.2 Quantile regression 9.3 Finite mixture models 9.4 Nonparametric regression 9.5 Conditional density estimator 9.6 Stata resources 10 Endogeneity 10.1 Introduction 10.2 Endogeneity in linear models 10.3 Endogeneity with a binary endogenous variable 10.4 GMM 10.5 Stata resources 11 Design effects 11.1 Introduction 11.2 Features of sampling designs 11.3 Methods for point estimation and inference 11.4 Empirical examples 11.5 Conclusion 11.6 Stata resources References.