This work focuses on three types of Stata programming: do-file programming, ado-file programming, and Mata functions that work in conjunction with do- and ado-files. It explains how to usefully automate work with Stata and how to use Stata more effectively through programming on one or more of these levels. After presenting elementary concepts of the command-line interface and commonly used tools for working with programs and data sets, the text follows a unique format by offering "cookbook" chapters after each main chapter. These cookbook chapters look at how to perform a specific programming task with Stata and provide a complete solution to the problem. The text also includes numerous examples of Mata, Stata's matrix programming language.

Christopher F. Baum’s An Introduction to Stata Programming is worthwhile for anyone wanting to learn about programming in Stata. For the beginner, Baum assumes only that the user is familiar with Stata, and so he builds up accordingly. For the more advanced Stata programmer, the book introduces Stata’s Mata programming language and provides optimization tips for day-to-day work. All readers will find better, new ways to approach old tasks.

Baum steps the reader through the three levels of Stata programming. First up are do-files. Though often thought of as simple batch files, do-files support both loops and conditional execution, and hence can be used for automation as well as reproducibility. While giving examples of do-file programming, Baum introduces useful but often-overlooked Stata constructions.

Next come ado-files, which are used to extend Stata by creating new commands that share the syntax and behavior of official commands. Baum gives an example of how to write a simple additional command for Stata, complete with documentation and certification. After writing the simple command, Baum then shows how users can write their own custom estimation commands by using both Stata’s built-in numerical maximum likelihood estimation routine, ml, and its built-in nonlinear least-squares routines, nl and nlsur.

Finishing up the book are two chapters on programming in Mata, which is Stata’s matrix programming language. Mata programs are integrated into ado-files to build a custom estimation routine that is optimized for speed and numerical stability. While stepping through these structures, Baum weaves in the details that are needed to become an expert at Stata programming, so readers will also learn more about Stata itself while learning the tools for programming.

Baum approaches each topic by first explaining the background and need for the topic, then looking at the basic usage and examples, and finally examining use within larger, more applied “cookbook” examples. Many of his examples come from questions posed on the Statalist listserver, so they address complexities of interest to a broad range of Stata users. The programming examples cover an array of topics, illustrate some of Stata’s built-in tools (such as the resampling techniques of bootstrapping and jackknifing), and offer solutions to tricky data management questions.

The breadth and depth of this book make it a necessity for anyone interested in programming in Stata.