Comment from the Stata technical group
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
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
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.
This book provides an excellent resource for both teaching and learning
modern microeconometric practice, using the most popular software
package in this area. The coverage includes discrete choice models and
models for panel data, as well as linear regression and instrumental
variables methods. I particularly like the material on handling large
datasets and developing efficient programs within Stata, which provide the
reader with an invaluable introduction to good practice in empirical
Prof. Steve Bond
Nuffield College, Oxford
and Institute for Fiscal Studies (IFS) London
Kit Baum provides students and researchers a hands-on guide to modern
econometric techniques by means of many well-documented examples in Stata.
The examples are also useful templates for those who need to write Stata
routines for their own work. Treatment and transformation of cross-section,
time-series, and panel data are carefully explained. The coverage of the
text is broad and up to date. An Introduction to Modern Econometrics
Using Stata is a valuable companion to undergraduate- and graduate-level
Department of Economics, University of Michigan
Christopher Baum’s An Introduction to Modern Econometrics Using Stata is
probably the only econometrics text published to date that pays serious
attention to reproducibility of research and systematic data validation
using Stata’s data audit commands along with do-file and programming
capabilities. Economic and financial consultants will find this text to be
an invaluable guide to using Stata for creating reproducible, error-free
data and econometric analysis, as well as quality graphic presentations. The
book is comprehensive and easy to follow, with substantive coverage of
econometric theory and applications using the full array of Stata’s
capabilities. This text should serve as an excellent learning and reference
guide for every consultant.
Zaur Rzakhanov, Ph.D.
Associate, Analysis Group Inc.
This book is a wonderful complement to the Stata technical manuals. It
provides a wealth of practical tips and sample applications that help the
intermediate-level Stata user advance in making the most efficient use of
Stata. It is thoughtfully organized along the lines of an econometrics
textbook, allowing practitioners to find relevant and useful commands,
procedures, and examples by topics that are familiar and immediate. It also
includes a most helpful appendix for novice programmers that will
expedite their development into proficient Stata programmers. This book is a
must-have reference for any organization that needs to train practitioners
of econometrics in the use of Stata.
For too long there has been a hole in the field between econometrics
textbooks, which focus on theory but give little practical guidance to the
day-to-day realities of economic research, and software manuals, which
provide detail but little analytical context. Researchers, analysts, and
students have no single source to turn to and often waste valuable time
and effort reinventing the wheel. This book brings it all together and
gives the researcher a huge step up on the learning curve. It perhaps should
have been subtitled “How to perform high-quality empirical research
using Stata.” It addresses topics in the order that real-world
research is performed, beginning with the data-management and
quality-control issues that a researcher must contend with every day and
then proceeding to the econometric tools used for most empirical analyses. A
researcher or a research analyst reading this book would learn insights and
tricks of the trade that would otherwise take years to accumulate. Common
errors (such as those resulting from many-to-many merges) are pointed out.
Useful tips (such as the use of local macros) are discussed. Efficient and
robust programming is encouraged throughout. This book should be required
reading for any empirical researcher or research analyst interested in
developing a high-quality research process.
Dr. Paul Liu
The Brattle Group