*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** and**replace**, **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 **ivreg2 **command.

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.

© Copyright 1996–2023 StataCorp LLC

**Illustrations**

**Preface** (PDF)

** Notation and typography**

1.2 Installing the necessary software

1.3 Installing the support materials

2.1 The basics

2.1.2 Variable types

2.1.3 _n and _N

2.1.4 generate and replace

2.1.5 sort and gsort

2.1.6 if exp and in range

2.1.7 Using if exp with indicator variables

2.1.8 Using if exp versus by varlist: with statistical commands

2.1.9 Labels and notes

2.1.10 The varlist

2.1.11 drop and keep

2.1.12 rename and renvars

2.1.13 The save command

2.1.14 insheet and infile

2.2 Common data transformations

2.2.2 Recoding discrete and continuous variables

2.2.3 Handling missing data

2.2.4 String-to-numeric conversion and vice versa

2.2.5 Handling dates

2.2.6 Some useful functions for generate or replace

2.2.7 The egen command

egen functions from the user community

2.2.8 Computation for by-groups

2.2.9 Local macros

2.2.10 Looping over variables: forvalues and foreach

2.2.11 Scalars and matrices

2.2.12 Command syntax and return values

3.2 Time-series data

3.3 Pooled cross-sectional time-series data

3.4 Panel data

3.5 Tools for manipulating panel data

3.5.2 Other transforms of panel data

3.5.3 Moving-window summary statistics and correlations

3.6 Combining cross-sectional and time-series datasets

3.7 Creating long-format datasets with append

3.7.2 The dangers of many-to-many merges

3.8 The reshape command

3.9 Using Stata for reproducible research

3.9.2 Data validation: assert and duplicates

4.2 Computing linear regression estimates

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

4.3.2 The ANOVA table: ANOVA F and R-squared

4.3.3 Adjusted R-squared

4.3.4 The coefficient estimates and beta coefficients

4.3.5 Regression without a constant term

4.3.6 Recovering estimation results

4.3.7 Detecting collinearity in regression

4.4 Presenting regression estimates

4.5 Hypothesis tests, linear restrictions, and constrained least squares

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

4.7 Computing marginal effects

4.A Appendix: Regression as a least-squares estimator

4.B Appendix: The large-sample VCE for linear regression

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 The asymmetry of specification error

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 DFBETA statistic

5.3 Endogeneity and measurement error

6.1 The generalized linear regression model

6.1.2 The robust estimator of VCE

6.1.3 The cluster estimator of VCE

6.1.4 The Newey–West estimator of VCE

6.1.5 The generalized-least squares estimator

6.2 Heteroskedasticity in the error distribution

6.2.1 Heteroskedasticity related to scale

FGLS estimation

6.2.2 Heteroskedasticity between groups of observations

FGLS estimation

6.2.3 Heteroskedasticity in grouped data

6.3 Serial correlation in the error distribution

6.3.2 FGLS estimation with serial correlation

7.1 Testing for significance of a qualitative factor

7.1.2 Regression with two qualitative measures

7.2 Regression with qualitative and quantitative factors

7.3 Seasonal adjustment with indicator variables

7.4 Testing for structural stability and structural change

7.4.2 Structural change in a time-series model

8.2 Endogeneity in economic relationships

8.3 2SLS

8.4 The ivreg command

8.5 Identification and tests of overidentifying restrictions

8.6 Computing IV estimates 8.7 ivreg2 and GMM estimation

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 HAC standard errors

8.8 Testing and overidentifying restrictions in GMM

8.9 Testing for heteroskedasticity in the IV context

8.10 Testing the relevance of instruments

8.11 Durbin–Wu–Hausman tests for endogeneity in IV estimation

8.A Appendix: Omitted-variables bias

8.B Appendix: Measurement error

9.1 FE and RE models

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 Prediction from one-way FE and RE

9.2 IV models for panel data

9.3 Dynamic panel-data models

9.4 Seemingly unrelated regression models

9.5 Moving-window regression estimates

10.1 Binomial logit and probit models

10.1.2 Marginal effects and predictions

Binomial logit and grouped logit

10.1.3 Evaluating specification and goodness of fit

10.2 Ordered logit and probit models

10.3 Truncated regression and tobit models

10.3.2 Censoring

10.4 Incidental truncation and sample-selection models

10.5 Bivariate probit and probit with selection

A.1 Inputting data from ASCII text files and spreadsheets

A.1.1 Handling text files

The insheet command

A.1.2 Accessing data stored in spreadsheets

A.1.3 Fixed-format data files

A.2 Importing data from other package formats

B.1 Local and global macros

B.1.2 Extended macro functions and list functions

B.2 Scalars

B.3 Loop constructs

B.4 Matrices

B.5 return and ereturn

B.6 The program and syntax statements

B.7 Using Mata functions in Stata programs

**References**

**Author index** (PDF)

**Subject index** (PDF)

© Copyright 1996–2023 StataCorp LLC