An Introduction to Stata Programming

Figures
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 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
	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, and 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
	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() functions
References
Author index
Subject index