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Author: Christopher F. Baum
Publisher: Stata Press
Copyright: 2009
ISBN-10: 1-59718-045-9
ISBN-13: 978-1-59718-045-0
Pages: 362; paperbackAuthor: Christopher F. Baum
Publisher: Stata Press
Copyright: 2009
ISBN-10: 1-59718-045-9
ISBN-13: 978-1-59718-045-0
Pages: 362; paperback
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
2.5 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 data from ASCII text files and spreadsheets
Handling text files
Free format versus fixed format
The insheet 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 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
6.4 reshape to perform rowwise computation
6.5 Adding computed statistics to presentation-quality tables
6.5.1 Presenting marginal effects rather than coefficients
6.6 Generating time-series data at a lower frequency
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 xi prefix
7.2.3 The statsby prefix
7.2.4 The rolling prefix
7.2.5 The simulate and permute prefix
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
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 outsheet, 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 Tabulating downloads from the Statistical Software Components archive
10.5 Extracting data from graph files’ sersets
10.6 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, simulate, bootstrap, and jackknife
Writing an ml-based command
11.13.1 Programs for the nl and nlsur commands
11.13.2 Programs for the simulate, bootstrap, and jackknife prefixes
11.14 Guidelines for Stata ado-file programming style
11.14.1 Presentation
11.14.2 Helpful Stata features
11.14.3 Respect for datasets
11.14.4 Speed and efficiency
11.14.5 Reminders
11.14.6 Style in the large
11.14.7 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 single longest spell
13 Mata functions for 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 Function components
13.3.1 Arguments
13.3.2 Variables
13.3.3 Saved results
13.4 Calling Mata functions
13.5 Mata’s st_ interface functions
13.5.1 Data access
13.5.2 Access to locals, globals, scalars, and matrices
13.5.3 Access to Stata variables’ attributes
13.6 Example: st_ interface function usage
13.7 Example: Matrix operations
13.7.1 Extending the command
13.8 Creating arrays of temporary objects with pointers
13.9 Structures
13.10 Additional Mata features
13.10.1 Macros in Mata functions
13.10.2 Compiling Mata functions
13.10.3 Building and maintaining an object library
13.10.4 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 Computing the seemingly unrelated regression estimator
14.9 A GMM-CUE estimator using Mata’s optimize() functions
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