Health Econometrics Using Stata 1st Edition by Partha Deb, Edward Norton, Willard Manning 1597182281 9781597182287

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ISBN 10: 1597182281
ISBN 13: 9781597182287
Author: Partha Deb, Edward C. Norton, Willard G. Manning

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.

Health Econometrics Using Stata 1st Table of contents:

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
 2.3.1 A laboratory experiment
 2.3.2 Randomization
 2.3.3 Covariate adjustment
2.4 Regression estimates of treatment effects
 2.4.1 Linear regression
 2.4.2 Nonlinear regression
2.5 Incremental and marginal effects
2.6 Model selection
 2.6.1 In-sample model selection
 2.6.2 Cross-validation
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
 4.3.1 Marginal and incremental effects
 4.3.2 Graphical representation of marginal and incremental effects
 4.3.3 Treatment effects
4.4 Consequences of misspecification
 4.4.1 Example: A quadratic specification
 4.4.2 Example: An exponential specification
4.5 Visual checks
 4.5.1 Artificial-data example of visual checks
 4.5.2 MEPS example of visual checks
4.6 Statistical tests
 4.6.1 Pregibon’s link test
 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 Generalized linear models
5.1 Introduction
5.2 GLM framework
 5.2.1 GLM assumptions
 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
 5.8.1 AIC and BIC
 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 Log and Box–Cox models
6.1 Introduction
6.2 Log models
 6.2.1 Log model estimation and interpretation
6.3 Retransformation from ln(y) to raw scale
 6.3.1 Error retransformation and model predictions
 6.3.2 Marginal and incremental effects
6.4 Comparison of log models to GLM
6.5 Box–Cox models
 6.5.1 Box–Cox example
6.6 Stata resources

7 Models for continuous outcomes with mass at zero
7.1 Introduction
7.2 Two-part models
 7.2.1 Expected values and marginal and incremental effects
7.3 Generalized tobit
 7.3.1 Full-information maximum likelihood and limited-information maximum likelihood
7.4 Comparison of two-part and generalized tobit models
 7.4.1 Examples that show similarity of marginal effects
7.5 Interpretation and marginal effects
 7.5.1 Two-part model example
 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
 7.6.1 The tobit model
 7.6.2 Why tobit is used sparingly
 7.6.3 One-part models
7.7 Statistical tests
7.8 Stata resources

8 Count models
8.1 Introduction
8.2 Poisson regression
 8.2.1 Poisson MLE
 8.2.2 Robustness of the Poisson regression
 8.2.3 Interpretation
 8.2.4 Is Poisson too restrictive?
8.3 Negative binomial models
 8.3.1 Examples of negative binomial models
8.4 Hurdle and zero-inflated count models
 8.4.1 Hurdle count models
 8.4.2 Zero-inflated models
8.5 Truncation and censoring
 8.5.1 Truncation
 8.5.2 Censoring
8.6 Model comparisons
 8.6.1 Model selection
 8.6.2 Cross-validation
8.7 Conclusion
8.8 Stata resources

9 Models for heterogeneous effects
9.1 Introduction
9.2 Quantile regression
 9.2.1 MEPS examples
 9.2.2 Extensions
9.3 Finite mixture models
 9.3.1 MEPS example of healthcare expenditures
 9.3.2 MEPS example of healthcare use
9.4 Nonparametric regression
 9.4.1 MEPS examples
9.5 Conditional density estimator
9.6 Stata resources

10 Endogeneity
10.1 Introduction
10.2 Endogeneity in linear models
 10.2.1 OLS is inconsistent
 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
 10.3.1 Additional considerations
10.4 GMM
10.5 Stata resources

11 Design effects
11.1 Introduction
11.2 Features of sampling designs
 11.2.1 Weights
 11.2.2 Clusters and stratification
 11.2.3 Weights and clustering in natural experiments
11.3 Methods for point estimation and inference
 11.3.1 Point estimation
 11.3.2 Standard errors
11.4 Empirical examples
 11.4.1 Survey design setup
 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

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Tags: Health Econometrics, Stata, Partha Deb, Edward Norton, Willard Manning