An Introduction to Stata Programming, Second Edition

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Christopher F. Baum's An Introduction to Stata Programming, Second Edition, is a great reference for anyone who wants to learn Stata programming.

 

Baum assumes readers have some familiarity with Stata, but readers who are new to programming will find the book accessible. He begins by introducing programming concepts and basic tools. More advanced programming tools such as structures and pointers and likelihood-function evaluators using Mata are gradually introduced throughout the book alongside examples.

 

This new edition reflects some of the most important statistical tools added since Stata 10. Of note are factor variables and operators, the computation of marginal effects, marginal means, and predictive margins using margins, the use of gmm to implement generalized method of moments estimation, and the use of suest for seemingly unrelated estimation.

 

As in the previous edition of the book, Baum steps the reader through the three levels of Stata programming. He starts with do-files. Do-files are powerful batch files that support loops and conditional statements and are ideal to automate your workflow as well as to guarantee reproducibility of your work.

 

He then delves into 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 command to calculate percentiles and the range of a variable, complete with documentation and certification.

 

After introducing the fundamentals of command development, Baum shows users how these concepts can be applied to help them write their own custom estimation commands by using Stata's built-in numerical maximum-likelihood estimation routine, ml, its built-in nonlinear least-squares routines, nl and nlsur, and its built-in generalized method of moments estimation routine.

 

Finally, he introduces Mata, 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. Baum briefly discusses how ado-file programming concepts relate to Mata functions and objects. He also explains some of the advantages of using Mata for certain programming tasks. Baum introduces concepts by providing the background and importance of the topic, presents common uses and examples, and then concludes with larger, more applied examples he refers to as “cookbook recipes”.

 

Many of the examples are of particular interest because they arose from frequently asked questions from Stata users. If you want to understand basic Stata programming or want to write your own routines and commands using advanced Stata tools, Baum's book is a great reference.

 

Christopher F. Baum is a Professor of Economics and Social Work at Boston College, where he codirects the undergraduate minor in scientific computation. Baum has taught econometrics for many years, using Stata extensively. He has over 40 years of experience with computer programming in a variety of languages and has authored or coauthored several widely used Stata commands over the past 12 years. He is the author of An Introduction to Modern Econometrics Using Stata, an associate editor of the Stata Journal, and a participant in many Stata Users Group meetings in the United States and Europe.

List of figures
List of tables
Preface (PDF)
Acknowledgments
Notation and typography
1 Why should you become a Stata programmer?
Do-file programming 
Ado-file programming 
Mata programming for ado-files 
1.1 Plan of the book 
1.2 Installing the necessary software 
2 Some elementary concepts and tools
2.1 Introduction 
2.1.1 What you should learn from this chapter 
2.2 Navigational and organizational issues 
2.2.1 The current working directory and profile.do 
2.2.2 Locating important directories: sysdir and adopath 
2.2.3 Organization of do-files, ado-files, and data files 
2.3 Editing Stata do- and ado-files 
2.4 Data types 
2.4.1 Storing data efficiently: The compress command 
2.4.2 Date and time handling 
2.4.3 Time-series operators 
2.4.4 Factor variables and operators 
2.5 Handling errors: The capture command 
2.6 Protecting the data in memory: The preserve and restore commands 
2.7 Getting your data into Stata 
2.7.1 Inputting and importing data 
Handling text files 
Free format versus fixed format 
The import delimited command 
Accessing data stored in spreadsheets 
Fixed-format data files 
2.7.2 Importing data from other package formats 
2.8 Guidelines for Stata do-file programming style 
2.8.1 Basic guidelines for do-file writers 
2.8.2 Enhancing speed and efficiency 
2.9 How to seek help for Stata programming 
3 Do-file programming: Functions, macros, scalars, and matrices
3.1 Introduction 
3.1.1 What you should learn from this chapter 
3.2 Some general programming details 
3.2.1 The varlist 
3.2.2 The numlist 
3.2.3 The if exp and in range qualifiers 
3.2.4 Missing data handling 
Recoding missing values: The mvdecode and mvencode commands 
3.2.5 String-to-numeric conversion and vice versa 
Numeric-to-string conversion 
Working with quoted strings 
3.3 Functions for the generate command 
3.3.1 Using if exp with indicator variables 
3.3.2 The cond() function 
3.3.3 Recoding discrete and continuous variables 
3.4 Functions for the egen command 
Official egen functions 
egen functions from the user community 
3.5 Computation for by-groups 
3.5.1 Observation numbering: _n and _N 
3.6 Local macros 
3.7 Global macros 
3.8 Extended macro functions and macro list functions 
3.8.1 System parameters, settings, and constants: creturn 
3.9 Scalars 
3.10 Matrices 
4 Cookbook: Do-file programming I
4.1 Tabulating a logical condition across a set of variables 
4.2 Computing summary statistics over groups 
4.3 Computing the extreme values of a sequence 
4.4 Computing the length of spells 
4.5 Summarizing group characteristics over observations 
4.6 Using global macros to set up your environment 
4.7 List manipulation with extended macro functions 
4.8 Using creturn values to document your work 
5 Do-file programming: Validation, results, and data management
5.1 Introduction 
5.1.1 What you should learn from this chapter 
5.2 Data validation: The assert, count, and duplicates commands 
5.3 Reusing computed results: The return and ereturn commands 
5.3.1 The ereturn list command 
5.4 Storing, saving, and using estimated results 
5.4.1 Generating publication-quality tables from stored estimates 
5.5 Reorganizing datasets with the reshape command 
5.6 Combining datasets 
5.7 Combining datasets with the append command 
5.8 Combining datasets with the merge command 
5.8.1 The one-to-one match-merge 
5.8.2 The dangers of many-to-many merges 
5.9 Other data management commands 
5.9.1 The fillin command 
5.9.2 The cross command 
5.9.3 The stack command 
5.9.4 The separate command 
5.9.5 The joinby command 
5.9.6 The xpose command 
6 Cookbook: Do-file programming II
6.1 Efficiently defining group characteristics and subsets 
6.1.1 Using a complicated criterion to select a subset of observations 
6.2 Applying reshape repeatedly 
6.3 Handling time-series data effectively 
6.3.1 Working with a business-daily calendar
6.4 reshape to perform rowwise computation 
6.5 Adding computed statistics to presentation-quality tables 
6.6 Presenting marginal effects rather than coefficients 
6.6.1 Graphing marginal effects with marginsplot
6.7 Generating time-series data at a lower frequency 
6.8 Using suest and gsem to compare estimates from nonoverlapping samples 
6.9 Using reshape to produce forecasts from a VAR or VECM 
6.10 Working with IRF files
7 Do-file programming: Prefixes, loops, and lists
7.1 Introduction 
7.1.1 What you should learn from this chapter 
7.2 Prefix commands 
7.2.1 The by prefix 
7.2.2 The statsby prefix 
7.2.3 The xi prefix and factor-variable notation
7.2.4 The rolling prefix 
7.2.5 The simulate and permute prefixes 
7.2.6 The bootstrap and jackknife prefixes 
7.2.7 Other prefix commands 
7.3 The forvalues and foreach commands 
8 Cookbook: Do-file programming III
8.1 Handling parallel lists 
8.2 Calculating moving-window summary statistics 
8.2.1 Producing summary statistics with rolling and merge 
8.2.2 Calculating moving-window correlations 
8.3 Computing monthly statistics from daily data 
8.4 Requiring at least n observations per panel unit 
8.5 Counting the number of distinct values per individual 
8.6 Importing multiple spreadsheet pages
9 Do-file programming: Other topics
9.1 Introduction 
9.1.1 What you should learn from this chapter 
9.2 Storing results in Stata matrices 
9.3 The post and postfile commands 
9.4 Output: The export delimited, outfile, and file commands 
9.5 Automating estimation output 
9.6 Automating graphics 
9.7 Characteristics 
10 Cookbook: Do-file programming IV
10.1 Computing firm-level correlations with multiple indices 
10.2 Computing marginal effects for graphical presentation 
10.3 Automating the production of LATEX tables 
10.4 Extracting data from graph files’ sersets 
10.5 Constructing continuous price and returns series 
11 Ado-file programming
11.1 Introduction 
11.1.1 What you should learn from this chapter 
11.2 The structure of a Stata program 
11.3 The program statement 
11.4 The syntax and return statements 
11.5 Implementing program options 
11.6 Including a subset of observations 
11.7 Generalizing the command to handle multiple variables 
11.8 Making commands byable 
Program properties 
11.9 Documenting your program 
11.10 egen function programs 
11.11 Writing an e-class program 
11.11.1 Defining subprograms 
11.12 Certifying your program 
11.13 Programs for ml, nl, nlsur
Maximum likelihood estimation of distributions' parameters
11.13.1 Writing an ml-based command 
11.13.2 Programs for the nl and nlsur commands 
11.14 Programs for gmm 
11.15 Programs for the simulate, bootstrap, and jackknife prefixes 
11.16 Guidelines for Stata ado-file programming style
11.16.1 Presentation 
11.16.2 Helpful Stata features 
11.16.3 Respect for datasets 
11.16.4 Speed and efficiency 
11.16.5 Reminders 
11.16.6 Style in the large 
11.16.7 Use the best tools 
12 Cookbook: Ado-file programming
12.1 Retrieving results from rolling 
12.2 Generalization of egen function pct9010() to support all pairs of quantiles 
12.3 Constructing a certification script 
12.4 Using the ml command to estimate means and variances 
12.4.1 Applying equality constraints in ml estimation 
12.5 Applying inequality constraints in ml estimation 
12.6 Generating a dataset containing the longest spell 
12.7 Using suest on a fixed-effects model
13 Mata functions for do-file and ado-file programming
13.1 Mata: First principles 
13.1.1 What you should learn from this chapter 
13.2 Mata fundamentals 
13.2.1 Operators 
13.2.2 Relational and logical operators 
13.2.3 Subscripts 
13.2.4 Populating matrix elements 
13.2.5 Mata loop commands 
13.2.6 Conditional statements 
13.3 Mata's st_ interface functions
13.3.1 Data access 
13.3.2 Access to locals, globals, scalars, and matrices 
13.3.3 Access to Stata variables' attributes 
13.4 Calling Mata with a single command line 
13.5 Components of a Mata Function
13.5.1 Arguments 
13.5.2 Variables 
13.5.3 Stored results 
13.6 Calling Mata functions 
13.7 Example: st_interface function usage 
13.8 Example: Matrix operations 
13.8.1 Extending the command
13.9 Mata-based likelihood function evaluators 
13.10 Creating arrays of temporary objects with pointers 
13.11 Structures 
13.12 Additional Mata features
13.12.1 Macros in Mata functions 
13.12.2 Associative arrays in Mata functions 
13.12.3 Compiling Mata functions 
13.12.4 Building and maintaining an object library 
13.12.5 A useful collection of Mata routines
14 Cookbook: Mata function programming
14.1 Reversing the rows or columns of a Stata matrix 
14.2 Shuffling the elements of a string variable 
14.3 Firm-level correlations with multiple indices with Mata 
14.4 Passing a function to a Mata function 
14.5 Using subviews in Mata 
14.6 Storing and retrieving country-level data with Mata structures 
14.7 Locating nearest neighbors with Mata 
14.8 Using a permutation vector to reorder results 
14.9 Producing LATEX tables from svy results 
14.10 Computing marginal effects for quantile regression 
14.11 Computing the seemingly unrelated regression estimator 
14.12 A GMM-CUE estimator using Mata's optimize() function
References