An Introduction to Modern Econometrics Using Stata, by Christopher F. Baum, successfully bridges the gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.
The first three chapters are dedicated to the basic skills needed to effectively use Stata: loading data into Stata; using commands like generate andreplace, egen, and sort to manipulate variables; taking advantage of loops to automate tasks; and creating new datasets by using merge and append. Baum succinctly yet thoroughly covers the elements of Stata that a user must learn to become proficient, providing many examples along the way.
Chapter 4 begins the core econometric material of the book and covers the multiple linear regression model, including efficiency of the ordinary least-squares estimator, interpreting the output from regress, and point and interval prediction. The chapter covers both linear and nonlinear Wald tests, as well as constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models.
Chapters 5 and 6 focus on consequences of failures of the linear regression model’s assumptions. Chapter 5 addresses topics like omitted-variable bias, misspecification of functional form, and outlier detection. Chapter 6 is dedicated to non-independently and identically distributed errors, and it introduces the Newey–West and Huber/White covariance matrices, as well as feasible generalized least-squares estimation in the presence of heteroskedasticity or serial correlation. Chapter 7 is dedicated to the use of indicator variables and interaction effects.
Instrumental-variables estimation has been an active area of research in econometrics, and chapter 8 commendably addresses issues like weak instruments, underidentification, and generalized method-of-moments estimation. In this chapter, Baum extensively uses his wildly popular ivreg2command.
The last two chapters briefly introduce panel-data analysis and discrete and limited-dependent variables. Two appendices detail importing data into Stata and Stata programming. As in all chapters, Baum presents many Stata examples.
An Introduction to Modern Econometrics Using Stata can serve as a supplementary text in both undergraduate- and graduate-level econometrics courses, and the book’s examples will help students quickly become proficient in Stata. The book is also useful to economists and businesspeople wanting to learn Stata by using practical examples.
Christopher F. Baum is an economist at Boston College, where he codirects the undergraduate minor in scientific computation. He is an associate editor of the Stata Journal and co-organizer of Stata Users Group meetings in Boston. Baum has coauthored many Stata routines and maintains the Statistical Software Components Archive of downloadable Stata components. He has taught econometrics at the undergraduate and graduate levels, making extensive use of Stata, for many years.
Illustrations
Preface
Notation and typography
1. INTRODUCTION
An overview of Stata’s distinctive features
Installing the necessary software
Installing the support materials
2. WORKING WITH ECONOMIC AND FINANCIAL DATA IN STATA
The basics
The use command
Variable types
_n and _N
generate and replace
sort and gsort
if exp and in range
Using if exp with indicator variables
Using if exp versus by varlist: with statistical commands
Labels and notes
The varlist
drop and keep
rename and renvars
The save command
insheet and infile
Common data transformations
The cond() function
Recoding discrete and continuous variables
Handling missing data
mvdecode and mvencode
String-to-numeric conversion and vice versa
Handling dates
Some useful functions for generate or replace
The egen command
Official egen functions
egen functions from the user community
Computation for by-groups
Local macros
Looping over variables: forvalues and foreach
Scalars and matrices
Command syntax and return values
3. ORGANIZING AND HANDLING ECONOMIC DATA
Cross-sectional data and identifier variables
Time-series data
Time-series operators
Pooled cross-sectional time-series data
Panel data
Operating on panel data
Tools for manipulating panel data
Unbalanced panels and data screening
Other transforms of panel data
Moving-window summary statistics and correlations
Combining cross-sectional and time-series datasets
Creating long-format datasets with append
Using merge to add aggregate characteristics
The dangers of many-to-many merges
The reshape command
The xpose command
Using Stata for reproducible research
Using do-files
Data validation: assert and duplicates
4. LINEAR REGRESSION
Introduction
Computing linear regression estimates
Regression as a method-of-moments estimator
The sampling distribution of regression estimates
Efficiency of the regression estimator
Numerical identification of the regression estimates
Interpreting regression estimates
Research project: A study of single-family housing prices
The ANOVA table: ANOVA F and R-squared
Adjusted R-squared
The coefficient estimates and beta coefficients
Regression without a constant term
Recovering estimation results
Detecting collinearity in regression
Presenting regression estimates
Presenting summary statistics and correlations
Hypothesis tests, linear restrictions, and constrained least squares
Wald tests with test
Wald tests involving linear combinations of parameters
Joint hypothesis tests
Testing nonlinear restrictions and forming nonlinear combinations
Testing competing (nonnested) models
Computing residuals and predicted values
Computing interval predictions
Computing marginal effects
Appendix: Regression as a least-squares estimator
Appendix: The large-sample VCE for linear regression
5. SPECIFYING THE FUNCTIONAL FORM
Introduction
Specification error
Omitting relevant variables from the model
Specifying dynamics in time-series regression models
Graphically analyzing regression data
Added-variable plots
Including irrelevant variables in the model
The asymmetry of specification error
Misspecification of the functional form
Ramsey’s RESET
Specification plots
Specification and interaction terms
Outlier statistics and measures of leverage
The DFITS statistic
The DFBETA statistic
Endogeneity and measurement error
6. REGRESSION WITH NON-I.I.D. ERRORS
The generalized linear regression model
Types of deviations from i.i.d. errors
The robust estimator of VCE
The cluster estimator of VCE
The Newey–West estimator of VCE
The generalized-least squares estimator
The FGLS estimator
Heteroskedasticity in the error distribution
Heteroskedasticity related to scale
Testing for heteroskedasticity related to scale
FGLS estimation
Heteroskedasticity between groups of observations
Testing for heteroskedasticity between groups of observations
FGLS estimation
Heteroskedasticity in grouped data
FGLS estimation
Serial correlation in the error distribution
Testing for serial correlation
FGLS estimation with serial correlation
7. REGRESSION WITH INDICATOR VARIABLES
Testing for significance of a qualitative factor
Regression with one qualitative measure
Regression with two qualitative measures
Interaction effects
Regression with qualitative and quantitative factors
Testing for slope differences
Seasonal adjustment with indicator variables
Testing for structural stability and structural change
Constraints of continuity and differentiability
Structural change in a time-series model
8. INSTRUMENTAL-VARIABLES ESTIMATORS
Introduction
Endogeneity in economic relationships
2SLS
The ivreg command
Identification and tests of overidentifying restrictions
Computing IV estimates
ivreg2 and GMM estimation
The GMM estimator
GMM in a homoskedastic context
GMM and heteroskedasticity-consistent standard errors
GMM and clustering
GMM and HAC standard errors
Testing and overidentifying restrictions in GMM
Testing a subset of the overidentifying restrictions in GMM
Testing for heteroskedasticity in the IV context
Testing the relevance of instruments
Durbin–Wu–Hausman tests for endogeneity in IV estimation
Appendix: Omitted-variables bias
Appendix: Measurement error
Solving errors-in-variables problems
9. PANEL-DATA MODELS
FE and RE models
One-way FE
Time effects and two-way FE
The between estimator
One-way RE
Testing the appropriateness of RE
Prediction from one-way FE and RE
IV models for panel data
Dynamic panel-data models
Seemingly unrelated regression models
SUR with identical regressors
Moving-window regression estimates
10. MODELS OF DISCRETE AND LIMITED DEPENDENT VARIABLES
Binomial logit and probit models
The latent-variable approach
Marginal effects and predictions
Binomial probit
Binomial logit and grouped logit
Evaluating specification and goodness of fit
Ordered logit and probit models
Truncated regression and tobit models
Truncation
Censoring
Incidental truncation and sample-selection models
Bivariate probit and probit with selection
Binomial probit with selection
A. GETTING THE DATA INTO STATA
Inputting data from ASCII text files and spreadsheets
Handling text files
Free format versus fixed format
The insheet command
Accessing data stored in spreadsheets
Fixed-format data files
Importing data from other package formats
B. THE BASIC OF STATA PROGRAMMING
Local and global macros
Global macros
Extended macro functions and list functions
Scalars
Loop constructs
Foreach
Matrices
Return and ereturn
Ereturn list
The program and syntax statements
Using Mata functions in Stata programs
