List of figures
List of tables
Acknowledgements
Notation and typography
1 Introduction
1.1 An overview of Stata’s distinctive features
1.2 Installing the necessary software
1.3 Installing the support materials
2 Working with economic and financial data in Stata
2.1 The Stata workspace
2.1.1 Accessing a Stata dataset
2.1.2 Data types
2.1.3 _n and _N
2.1.4 The varlist
2.1.5 The numlist
2.1.6 if exp and in range
2.1.7 Stata’s fundamental design
2.1.8 sort and gsort
2.1.9 Command syntax
2.1.10 return list, ereturn list
2.2 Modify the workspace
2.2.1 generate and replace
Logical operators
2.2.2 egen
2.2.3 labels and notes data
2.2.4 drop and keep
2.2.5 rename
2.2.6 save, saveold, and compress
2.3 Factor variables
2.3.1 The interaction operator
2.3.2 The factorial interaction operator
2.4 Prefix operators
2.4.1 The by prefix
2.4.2 Computation for by-groups
2.4.3 The statsby prefix
2.4.4 The rolling prefix
2.5 Data management
2.5.1 Data input
2.5.2 Handling missing data
2.5.3 String-to-numeric conversion and vice versa
2.5.4 Recording discrete and continuous variables
2.5.5 Handling dates and times
2.5.6 Some useful functions for generate or replace
2.5.7 Data validation with assert, count, and duplicates
2.6 Local and global macros, scalars, and matrices
2.6.1 Local macros
2.6.2 Loops with forvalues and foreach
Nested loops
2.6.3 Extended macro functions and list functions
2.6.4 Global macros
2.6.5 System parameters, settings and constants
2.6.6 Scalars and matrices
2.7 The Mata language
2.8 Python integration with Stata
2.8.1 Julia integration with Stata
2.9 Adding capabilities to Stata
2.9.1 The SSC Archive
2.9.2 The Stata Journal
3 Organizing and handling economic data
3.1 Cross-sectional data and identifier variables
3.2 Time-series data
3.2.1 Time-series calendar
3.2.2 Time-series operators
3.3 Pooled cross-sectional time-series data
3.3.1 Combining cross-sectional and time-series datasets
3.3.2 Creating long-format datasets and time-series datasets
3.3.3 Using merge to add aggregate characteristics
3.3.4 Using joinby rather than merge
3.3.5 Other useful data management commands
fillin
cross
stack
separate
3.4 Panel data
3.4.1 Operating on panel data
3.4.2 Unbalanced panels and data screening
3.4.3 Other transforms of panel data
3.4.4 Moving-window summary statistics and correlations
3.4.5 Reshaping the data
3.5 Working with frames
3.5.1 Working with linked datasets
3.6 Reporting descriptive statistics
3.6.1 dtable
3.6.2 collect
3.6.3 table
4 Linear regression
4.1 Introduction
4.2 Computing linear regression estimates
4.2.1 Regression as a method of moments estimator
4.2.2 The sampling distribution of regression estimates
4.2.3 Efficiency of the regression estimator
4.2.4 Numerical identification of the regression estimates
4.3 Interpreting regression estimates
4.3.1 Research project: A study of wage determination
4.3.2 The ANOVA table: ANOVA F and R2
4.3.3 Adjusted R2
4.3.4 The coefficient estimates and beta coefficients
4.3.5 Regression without a constant term
4.3.6 Detecting collinearity in regression
4.4 Presenting regression estimates
4.4.1 etable
4.4.2 estout
4.5 Hypothesis tests, linear restrictions, and constrained least squares
4.5.1 Wald tests with test
4.5.2 Wald tests involving linear combinations of parameters
4.5.3 Joint hypothesis tests
4.5.4 Testing nonlinear restrictions and forming nonlinear combinations
4.5.5 Testing competing (nonnested) models
4.6 Computing residuals and predicted values
4.6.1 Computing interval predictions
4.7 Computing predictive means and marginal effects
4.7.1 Average marginal effects
4.A Appendix: Regression as a least-squares estimator
4.B Appendix: The large-sample VCE for linear regression
5 Specifying the functional form
5.1 Introduction
5.2 Specification error
5.2.1 Omitting relevant variables from the model
5.2.2 Graphically analyzing regression data
5.2.3 Added-variable plots
5.2.4 Including irrelevant variables in the model
5.2.5 Model selection
Specifying dynamics in time-series regression model
5.2.6 Misspecification of the functional form
5.2.7 Ramsey's RESET
5.2.8 Specification plots
5.2.9 Specification and interaction terms
5.2.10 Outlier statistics and measures of leverage
The DFITS statistic
The DFBETA statistic
5.3 Choosing the functional form
5.4 Endogeneity and measurement error
5.4.1 Errors-in-variables
6 Variance–covariance matrix estimations
6.1 The generalized linear regression model
6.1.1 Types of deviations from the classical VCE
6.1.2 The robust estimator of VCE
6.1.3 The HC2 and HC3 estimator of the VCE
6.1.4 The cluster–robust estimator of VCE
6.1.5 The Newey–West estimator of VCE
6.1.6 The generalized-least squares estimator
The FGLS estimator
6.2 Heteroskedasticity in the error distribution
6.2.1 Heteroskedasticity related to scale
Testing for heteroskedasticity related to scale
FGLS estimation
6.2.2 Heteroskedasticity between groups of observations
Testing for heteroskedasticity between groups of observations
FGLS estimation
6.2.3 Heteroskedasticity in grouped data
FGLS estimation
6.3 Serial correlation in the error distribution
6.3.1 Testing for serial correlation
6.3.2 FGLS estimation with serial correlation
7 Regression with indicator variables
7.1 Testing for significance of a qualitative factor
7.1.1 Regression with one qualitative measure
7.1.2 Regression with two qualitative measures
Interaction effects
7.2 Regression with qualitative and quantitative factors
Testing for slope differences
7.3 Seasonal adjustment with indicator variables
7.4 Testing for structural stability and structural change
7.4.1 Constraints of continuity and differentiability
7.4.2 Structural change in a time-series model
8 Instrumental-variables estimators
8.1 Introduction
8.2 Endogeneity in economic relationships
8.3 2SLS
8.4 ivregress
8.5 Identification and tests of overidentifying restrictions
8.6 Computing IV estimates
8.7 GMM estimation and ivreg2
8.7.1 The GMM estimator
8.7.2 GMM in a homoskedastic context
8.7.3 GMM and heteroskedasticity-consistent standard errors
8.7.4 GMM and clustering
8.7.5 GMM and heteroskedasticity- and autocorrelation-consistent standard errors
8.8 Testing and overidentifying restrictions in GMM
8.8.1 Testing a subset of the overidentifying restrictions in GMM
8.9 Testing for heteroskedasticity in the IV context
8.10 Testing for serial correlation in the IV context
8.11 Testing the relevance of instruments
8.12 Durbin–Wu–Hausman tests for endogeneity in IV estimation
8.13 Heteroskedasticity-based estimation
8.13.1 The model and estimator
8.13.2 Implementing the estimator and tests
8.13.3 Illustration of ivreg2h
8.14 Control-function estimation
8.A Appendix: Omitted-variables bias
8.B Appendix: Measurement error
8.B.1 Solving errors-in-variables problems
9 Panel-data models
9.1 FE, RE, and CRE models
9.1.1 One-way FE
9.1.2 Time effects and two-way FE
9.1.3 The between estimator
9.1.4 One-way RE
9.1.5 Testing the appropriateness of RE
9.1.6 CRE
9.1.7 Prediction from one-way FE and RE
9.2 IV models for panel data
9.3 DPD models
9.4 SUR models
9.4.1 SUR with identical regressors
9.5 Moving-window regression estimates
10 Models of discrete and limited dependent variables
10.1 Binomial logit and probit models
10.1.1 The latent-variable approach
10.1.2 Marginal effects and predictions
Binomial probit
Binomial logit and grouped logit
10.1.3 Evaluating specification and goodness of fit
10.2 Fractional response models
10.3 Ordered logit and probit models
10.4 Models and count data
10.4.1 The Poisson model
10.4.2 The negative binomial model
10.4.3 Extended count-data models
10.5 Truncated regression and tobit models
10.5.1 Truncation
10.5.2 Censoring
10.6 Incidental truncation and sample-selection models
10.7 Bivariate probit and probit with selection
10.7.1 Binomial probit with selection
A Getting the data into Stata
A.1 Inputting data from ASCII text files and spreadsheets
A.1.1 Handling text files
Free format versus fixed format
import delimited
A.1.2 Accessing data stored in spreadsheets
A.1.3 Fixed-format data files
A.2 Importing data from other package formats
B The basics of Stata programming
B.1 The program and syntax statements
B.2 Using Mata functions in Stata programs
References
