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书籍名称:Health Econometrics Using Stata
出版社:Stata Press
作者: Partha Deb, Edward C. Norton, Willard G. Manning
语种: 英文
页数: 264
开本: 胶版纸
纸张:264 I S B N: 978-1-59718-228-7
装订: 平装


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.


    List of tables
    List of figures
    Notation and typography
    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
    Author index
    Subject index