An introduction to Stata Programming, Second Edition
by Christopher Baum
Christopher F. Baum's An Introduction to Stata Programming, Second Edition,
is a great reference for anyone that wants to learn Stata programming.
For those learning, Baum assumes familiarity with Stata and gradually
introduces more advanced programming tools. For the more advanced Stata
programmer, the book introduces Stata's Mata programming language and
optimization routines. This
new edition of the book reflects some of the most important statistical
tools added since Stata 10, when the book was introduced. 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. While giving examples of do-file
programming, Baum introduces useful programming tips and advice. 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 simple additional command for
Stata, complete with documentation and certification. After writing the
simple command, users can then learn how to write their own custom
estimation commands by using both 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. While discussing Mata,
Baum presents useful topics for advanced programming such as structures
and pointers and likelihood-function evaluators using Mata. Baum
introduces concepts by providing the background and importance for 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 in the book 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
TABLE OF CONTENTS
List of figures List of tables Preface 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
3.5.1 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
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
11.15 Programs for the simulate, bootstrap, and jackknife prefixes
11.16 Guidelines for Stata ado-file programming style11.16.1 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 functions13.3.1 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 Function13.5.1 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 features13.12.1 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
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