Health Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.
After the overview chapters, the book provides excellent introductions to a series of topics aimed specifically at those analyzing healthcare expenditure and use data. The basic topics of linear regression, the generalized linear model, and log and Box-Cox models are covered with a tight focus on the problems presented by these data. Using this foundation, the authors cover the more advanced topics of models for continuous outcome with mass points, count models, and models for heterogeneous effects. Finally, they discuss endogeneity and how to address inference questions using data from complex surveys.
The authors use their formidable experience to guide readers toward useful methods and away from less recommended ones. Their discussion of “health econometric myths” and the chapter presenting a framework for approaching health econometric estimation problems are especially useful for this aspect.
© Copyright 1996–2023 StataCorp LLC
List of tables
List of figures
Preface (PDF)
Notation and typography
1.2 Themes
1.3 Health econometric myths
1.4 Stata friendly
1.5 A useful way forward
2.2 Potential outcomes and treatment effects
2.3 Estimating ATEs
2.3.2 Randomization
2.3.3 Covariate adjustment
2.4 Regression estimates of treatment effects
2.4.2 Nonlinear regression
2.5 Incremental and marginal effects
2.6 Model selection
2.6.2 Cross-validation
2.7 Other issues
3.2 Overview of all variables
3.3 Expenditure and use variables
3.4 Explanatory variables
3.5 Sample dataset
3.6 Stata resources
4.2 The linear regression model
4.3 Marginal, incremental, and treatment effects
4.3.2 Graphical representation of marginal and incremental effects
4.3.3 Treatment effects
4.4 Consequences of misspecification
4.4.2 Example: An exponential specification
4.5 Visual checks
4.5.2 MEPS example of visual checks
4.6 Statistical tests
4.6.2 Ramsey’s RESET test
4.6.3 Modified Hosmer–Lemeshow test
4.6.4 Examples
4.6.5 Model selection using AIC and BIC
4.7 Stata resources
5.2 GLM framework
5.2.2 Parameter estimation
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
5.8.2 Test for the link function
5.8.3 Modified Park test for the distribution family
5.8.4 Extended GLM
5.9 Conclusions
5.10 Stata resources
6.2 Log models
6.3 Retransformation from ln(y) to raw scale
6.3.2 Marginal and incremental effects
6.4 Comparison of log models to GLM
6.5 Box–Cox models
6.6 Stata resources
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
7.5.2 Two-part model marginal effects
7.5.3 Two-part model marginal effects example
7.5.4 Generalized tobit interpretation
7.5.5 Generalized tobit example
7.6 Single-index models that accommodate zeros
7.6.2 Why tobit is used sparingly
7.6.3 One-part models
7.7 Statistical tests
7.8 Stata resources
8.2 Poisson regression
8.2.2 Robustness of the Poisson regression
8.2.3 Interpretation
8.2.4 Is Poisson too restrictive?
8.3 Negative binomial models
8.4 Hurdle and zero-inflated count models
8.4.2 Zero-inflated models
8.5 Truncation and censoring
8.5.2 Censoring
8.6 Model comparisons
8.6.2 Cross-validation
8.7 Conclusion
8.8 Stata resources
9.2 Quantile regression
9.2.2 Extensions
9.3 Finite mixture models
9.3.2 MEPS example of healthcare use
9.4 Nonparametric regression
9.5 Conditional density estimator
9.6 Stata resources
10.2 Endogeneity in linear models
10.2.2 2SLS
10.2.3 Specification tests
10.2.4 2SRI
10.2.5 Modeling endogeneity with ERM
10.3 Endogeneity with a binary endogenous variable
10.4 GMM
10.5 Stata resources
11.2 Features of sampling designs
11.2.2 Clusters and stratification
11.2.3 Weights and clustering in natural experiments
11.3 Methods for point estimation and inference
11.3.2 Standard errors
11.4 Empirical examples
11.4.2 Weighted sample means
11.4.3 Weighted least-squares regression
11.4.4 Weighted Poisson count model
11.5 Conclusion
11.6 Stata resources
References
Author index (PDF)
Subject index (PDF)
© Copyright 1996–2023 StataCorp LLC
