An Introduction to Stata Programming

Transcription

1 An Introduction to Stata Programming Second Edition CHRISTOPHER F. BAUM Department of Economics and School of Social Work Boston College A Stata Press Publication StataCorp LP College Station, Texas

2 Copyright c 2009, 2016 by StataCorp LP All rights reserved. First edition 2009 Second edition 2016 Published by Stata Press, 4905 Lakeway Drive, College Station, Texas Typeset in L A TEX2ε Printed in the United States of America ISBN-10: ISBN-13: Library of Congress Control Number: No part of this book may be reproduced, stored in a retrieval system, or transcribed, in any form or by any means electronic, mechanical, photocopy, recording, or otherwise without the prior written permission of StataCorp LP. Stata,, Stata Press, Mata,, and NetCourse are registered trademarks of StataCorp LP. Stata and Stata Press are registered trademarks with the World Intellectual Property Organization of the United Nations. L A TEX2ε is a trademark of the American Mathematical Society.

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4 Contents List of figures xvii List of tables xix Preface xxi Acknowledgments xxiii Notation and typography xxv 1 Why should you become a Stata programmer? 1 Do-file programming Ado-file programming Mata programming for ado-files Plan of the book Installing the necessary software Some elementary concepts and tools Introduction What you should learn from this chapter Navigational and organizational issues The current working directory and profile.do Locating important directories: sysdir and adopath Organization of do-files, ado-files, and data files Editing Stata do- and ado-files Data types Storing data efficiently: The compress command Date and time handling Time-series operators Factor variables and operators

5 viii Contents 2.5 Handling errors: The capture command Protecting the data in memory: The preserve and restore commands Getting your data into Stata 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 Importing data from other package formats Guidelines for Stata do-file programming style Basic guidelines for do-file writers Enhancing speed and efficiency How to seek help for Stata programming Do-file programming: Functions, macros, scalars, and matrices Introduction What you should learn from this chapter Some general programming details The varlist The numlist The if exp and in range qualifiers Missing-data handling Recoding missing values: The mvdecode and mvencode commands String-to-numeric conversion and vice versa Numeric-to-string conversion Working with quoted strings Functions for the generate command Using if exp with indicator variables The cond() function

6 Contents ix Recoding discrete and continuous variables Functions for the egen command Official egen functions egen functions from the user community Computation for by-groups Observation numbering: n and N Local macros Global macros Extended macro functions and macro list functions System parameters, settings, and constants: creturn Scalars Matrices Cookbook: Do-file programming I Tabulating a logical condition across a set of variables Computing summary statistics over groups Computing the extreme values of a sequence Computing the length of spells Summarizing group characteristics over observations Using global macros to set up your environment List manipulation with extended macro functions Using creturn values to document your work Do-file programming: Validation, results, and data management Introduction What you should learn from this chapter Data validation: The assert, count, and duplicates commands Reusing computed results: The return and ereturn commands The ereturn list command Storing, saving, and using estimated results Generating publication-quality tables from stored estimates Reorganizing datasets with the reshape command

7 x Contents 5.6 Combining datasets Combining datasets with the append command Combining datasets with the merge command The one-to-one match-merge The dangers of many-to-many merges Other data management commands The fillin command The cross command The stack command The separate command The joinby command The xpose command Cookbook: Do-file programming II Efficiently defining group characteristics and subsets Using a complicated criterion to select a subset of observations Applying reshape repeatedly Handling time-series data effectively Working with a business-daily calendar reshape to perform rowwise computation Adding computed statistics to presentation-quality tables Presenting marginal effects rather than coefficients Graphing marginal effects with marginsplot Generating time-series data at a lower frequency Using suest and gsem to compare estimates from nonoverlapping samples Using reshape to produce forecasts from a VAR or VECM Working with IRF files Do-file programming: Prefixes, loops, and lists Introduction What you should learn from this chapter

8 Contents xi 7.2 Prefix commands The by prefix The statsby prefix The xi prefix and factor-variable notation The rolling prefix The simulate and permute prefixes The bootstrap and jackknife prefixes Other prefix commands The forvalues and foreach commands Cookbook: Do-file programming III Handling parallel lists Calculating moving-window summary statistics Producing summary statistics with rolling and merge Calculating moving-window correlations Computing monthly statistics from daily data Requiring at least n observations per panel unit Counting the number of distinct values per individual Importing multiple spreadsheet pages Do-file programming: Other topics Introduction What you should learn from this chapter Storing results in Stata matrices The post and postfile commands Output: The export delimited, outfile, and file commands Automating estimation output Automating graphics Characteristics Cookbook: Do-file programming IV Computing firm-level correlations with multiple indices Computing marginal effects for graphical presentation

9 xii Contents 10.3 Automating the production of L A TEX tables Extracting data from graph files sersets Constructing continuous price and returns series Ado-file programming Introduction What you should learn from this chapter The structure of a Stata program The program statement The syntax and return statements Implementing program options Including a subset of observations Generalizing the command to handle multiple variables Making commands byable Program properties Documenting your program egen function programs Writing an e-class program Defining subprograms Certifying your program Programs for ml, nl, and nlsur Maximum likelihood estimation of distributions parameters Writing an ml-based command Programs for the nl and nlsur commands Programs for gmm Programs for the simulate, bootstrap, and jackknife prefixes Guidelines for Stata ado-file programming style Presentation Helpful Stata features Respect for datasets Speed and efficiency

10 Contents xiii Reminders Style in the large Use the best tools Cookbook: Ado-file programming Retrieving results from rolling Generalization of egen function pct9010() to support all pairs of quantiles Constructing a certification script Using the ml command to estimate means and variances Applying equality constraints in ml estimation Applying inequality constraints in ml estimation Generating a dataset containing the longest spell Using suest on a fixed-effects model Mata functions for do-file and ado-file programming Mata: First principles What you should learn from this chapter Mata fundamentals Operators Relational and logical operators Subscripts Populating matrix elements Mata loop commands Conditional statements Mata s st interface functions Data access Access to locals, globals, scalars, and matrices Access to Stata variables attributes Calling Mata with a single command line Components of a Mata function Arguments

11 xiv Contents Variables Stored results Calling Mata functions Example: st interface function usage Example: Matrix operations Extending the command Mata-based likelihood function evaluators Creating arrays of temporary objects with pointers Structures Additional Mata features Macros in Mata functions Associative arrays in Mata functions Compiling Mata functions Building and maintaining an object library A useful collection of Mata routines Cookbook: Mata function programming Reversing the rows or columns of a Stata matrix Shuffling the elements of a string variable Firm-level correlations with multiple indices with Mata Passing a function to a Mata function Using subviews in Mata Storing and retrieving country-level data with Mata structures Locating nearest neighbors with Mata Using a permutation vector to reorder results Producing L A TEX tables from svy results Computing marginal effects for quantile regression Computing the seemingly unrelated regression estimator A GMM-CUE estimator using Mata s optimize() functions

12 Contents xv References 397 Author index 403 Subject index 405

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14 Preface This book is a concise introduction to the art of Stata programming. It covers three types of programming that can be used in working with Stata: do-file programming, ado-file programming, and Mata functions that work in conjunction with do- and adofiles. Its emphasis is on the automation of your work with Stata and how programming on one or more of these levels can help you use Stata more effectively. In the development of these concepts, I do not assume that you have prior experience with Stata programming, although familiarity with the command-line interface is helpful. While examples are drawn from several disciplines, my background as an applied econometrician is evident in the selection of some sample problems. The introductory first chapter motivates the why: why should you invest time and effort into learning Stata programming? In chapter 2, I discuss elementary concepts of the commandline interface and describe some commonly used tools for working with programs and datasets. The format of the book may be unfamiliar to readers who have some familiarity with other books that help you learn how to use Stata. Beginning with chapter 4, each even-numbered chapter is a cookbook chapter containing several recipes, 47 in total. Each recipe poses a problem: how can I perform a certain task with Stata programming? The recipe then provides a complete worked solution to the problem and describes how the features presented in the previous chapter can be put to good use. You may not want to follow a recipe exactly from the cookbook; just as in cuisine, a minor variation on the recipe may meet your needs, or the techniques presented in that recipe may help you see how Stata programming applies to your specific problem. Most Stata users who delve into programming use do-files to automate and document their work. Consequently, the major focus of the book is do-file programming, covered in chapters 3, 5, 7, and 9. Some users will find that writing formal Stata programs, or adofiles, meets their needs. Chapter 11 is a concise summary of ado-file programming, with the cookbook chapter that follows presenting several recipes that contain developed adofiles. Stata s matrix programming language, Mata, can also be helpful in automating certain tasks. Chapter 13 presents a summary of Mata concepts and the key features that allow interchange of variables, scalars, macros, and matrices. The last chapter, cookbook chapter 14, presents several examples of Mata functions developed to work with ado-files. All the do-files, ado-files, Mata functions, and datasets used in the book s examples and recipes are available from the Stata Press website, as discussed in Notation and typography.

15 xxii Preface The second edition of this book contains several new recipes illustrating how dofiles, ado-files, and Mata functions can be used to solve programming problems. Several recipes have also been updated to reflect new features in Stata added between versions 10 and 14. The discussion of maximum-likelihood function evaluators has been significantly expanded in this edition. The new topics covered in this edition include factor variables and operators; use of margins, marginsplot, and suest; Mata-based likelihood function evaluators; and associative arrays.

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17 1 Why should you become a Stata programmer? This book provides an introduction to several contexts of Stata programming. I must first define what I mean by programming. You can consider yourself a Stata programmer if you write do-files, which are text files of sequences of Stata commands that you can execute with the do ([R] do) command, by double-clicking on the file, or by running them in the Do-file Editor ([R] doedit). You might also write what Stata formally defines as a program, which is a set of Stata commands that includes the program ([P] program) command. A Stata program, stored in an ado-file, defines a new Stata command. You can also use Stata s matrix programming language, Mata, to write routines in that language that are called by ado-files. Any of these tasks involves Stata programming. 1 With that set of definitions in mind, we must deal with the why: why should you become a Stata programmer? After answering that essential question, this text takes up the how: how you can become a more efficient user of Stata by using programming techniques, be they simple or complex. Using any computer program or language is all about efficiency getting the computer to do the work that can be routinely automated, reducing human errors, and allowing you to more efficiently use your time. Computers are excellent at performing repetitive tasks; humans are not. One of the strongest rationales for learning how to use programming techniques in Stata is the potential to shift more of the repetitive burden of data management, statistical analysis, and production of graphics to the computer. Let s consider several specific advantages of using Stata programming techniques in the three contexts listed above. 1. There are also specialized forms of Stata programming, such as dialog programming, scheme programming, and class programming. A user-written program can present a dialog, like any official Stata command, if its author writes a dialog file. The command can also be added to the User menu of Stata s graphical interface. For more information, see [P] dialog programming and [P] window programming. Graphics users can write their own schemes to set graphic defaults. See [G-4] schemes intro for details. Class programming allows you to write object-oriented programs in Stata. As [P] class indicates, this has primarily been used in Stata s graphics subsystem and graphical user interface. I do not consider these specialized forms of programming in this book. 1

18 2 Chapter 1 Why should you become a Stata programmer? Do-file programming Using a do-file to automate a specific data-management or statistical task leads to reproducible research and the ability to document the empirical research process. This reduces the effort needed to perform a similar task at a later point or to document for your coworkers or supervisor the specific steps you followed. Ideally, your entire research project should be defined by a set of do-files that execute every step, from the input of the raw data to the production of the final tables and graphs. Because a do-file can call another do-file (and so on), a hierarchy of do-files can be used to handle a complex project. The beauty of this approach is its flexibility. If you find an error in an earlier stage of the project, you need only to modify the code and then rerun that do-file and those following tobringtheproject uptodate. Forinstance, aresearchermay needtorespond to a review of her paper submitted months ago to an academic journal by revising the specification of variables in a set of estimated models and estimating new statistical results. If all the steps that produce the final results are documented by a set of dofiles, her task is straightforward. I argue that all serious users of Stata should gain some facility with do-files and the Stata commands that support repetitive use of commands. That advice does not imply that Stata s interactive capabilities should be shunned. Stata is a powerful and effective tool for exploratory data analysis and ad hoc queries about your data. But data-management tasks and the statistical analyses leading to tabulated results should not be performed with point-and-click tools that leave you without an audit trail of the steps you have taken. Ado-file programming On a second level, you may find that despite the breadth of Stata s official and userwritten commands, there are tasks you must repeatedly perform that involve variations on the same do-file. You would like Stata to have a command to perform those tasks. At that point, you should consider Stata s ado-file programming capabilities. Stata has great flexibility: a Stata command need be no more than a few lines of Stata code. Once defined, that command becomes a first-class citizen. You can easily write a Stata program, stored in an ado-file, that handles all the features of official Stata commands such as if exp, in range, and command options. You can (and should) write a help file that documents the program s operation for your benefit and for those with whom you share the code. Although ado-file programming requires that you learn how to use some additional commands used in that context, it can help you become more efficient in performing the data-management, statistical, or graphical tasks that you face. Mata programming for ado-files On a third level, your ado-files can perform some complicated tasks that involve many invocations of the same commands. Stata s ado-file language is easy to read and write,

19 1.1 Plan of the book 3 but it is interpreted. Stata must evaluate each statement and translate it into machine code. The Mata programming language (help mata) creates compiled code, which can run much faster than ado-file code. Your ado-file can call a Mata routine to carry out a computationally intensive task and return the results in the form of Stata variables, scalars, or matrices. Although you may think of Mata solely as a matrix language, it is actually a general-purpose programming language, suitable for many nonmatrixoriented tasks, such as text processing and list management. The level of Stata programming that you choose to attain and master depends on your needs and skills. As I have argued, the vast majority of interactive Stata users can and should take the next step of learning how to use do-files efficiently to take full advantage of Stata s capabilities and to save time. A few hours of investment in understanding the rudiments of do-file programming as covered in the chapters to follow will save you days or weeks over the course of a sizable research project. A smaller fraction of users may choose to develop ado-files. Many users find that those features lacking in official Stata are adequately provided by the work of members of the Stata user community who have developed and documented ado-files, sharing them via the Stata Journal, the Statistical Software Components (SSC) archive, 2 or their own user site. However, developing a reading knowledge of ado-file code is highly useful for many Stata users. It permits you to scrutinize ado-file code either that of official Stata or user-written code and more fully understand how it performs its function. In many cases, minor modifications to existing code may meet your needs. Mata has been embraced by programmers wishing to take advantage of its many features and its speed. Although this book does not discuss interactive use of Mata, I present two ways in which Mata can be used in ado-files: in one-liners to fulfill a single, specific task, and as functions to be called from ado-files. 1.1 Plan of the book The chapters of this book present the details of the three types of Stata programming discussed above, placing the greatest emphasis on effective use of do-file programming. Each fairly brief chapter on the structure of programming techniques is followed by a cookbook chapter. These chapters contain several recipes for the solution of a particular, commonly encountered problem, illustrating the necessary programming techniques to compose a solution. Like in a literal cookbook, the recipes here are illustrative examples; you are free to modify the ingredients to produce a somewhat different dish. The recipes as presented may not address your precise problem, but they should prove helpful in devising a solution as a variation on the same theme. 2. For details on the SSC (Boston College) archive of user-contributed routines, type help ssc.

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21 4 Cookbook: Do-file programming I This cookbook chapter presents for Stata do-file programmers several recipes using the programming features described in the previous chapter. Each recipe poses a problem and a worked solution. Although you may not encounter this precise problem, you should be able to recognize its similarities to a task that you would like to automate in a do-file. 4.1 Tabulating a logical condition across a set of variables The problem. When considering many related variables, you want to determine whether, for each observation, all variables satisfy a logical condition. Alternatively, you might want to know whether any satisfy that condition (for instance, taking on inappropriate values), or you might want to count how many of the variables satisfy the logical condition. 1 The solution. This would seem to be a natural application of egen ([D] egen), because that command already contains many rowwise functions to perform computations across variables. For instance, the anycount() function counts the number of variables in its varlist whose values for each observation match those of an integer numlist, whereas the rowmiss() and rownonmiss() functions tabulate the number of missing and nonmissing values for each observation, respectively. The three tasks above are all satisfied by egen functions from Nicholas Cox s egenmore package: rall(), rany(), and rcount(), respectively. Why not use those functions, then? Two reasons come to mind: First, recall that egen functions are interpreted code. Unlike the built-in functions accessed by generate, the logic of an egen function must be interpreted each time it is called. For a large dataset, the time penalty can be significant. Second, to use an egen function, you must remember that there is such a function, and you must remember its name. In addition to Stata s official egen functions, documented in the online help files, there are many user-written egen functions available, but you must track them down. For these reasons, current good programming practice suggests that you should avoid egen function calls in instances where the performance penalty might be an issue. This 1. This recipe relies heavily on Nicholas J. Cox s egenmore help file. 67