[stata资源分享]最新Stata Press书籍汇总（II）

Stata Press books are listed alphabetically by author.

Interpreting and Visualizing Regression Models Using Stata
Financial Econometrics Using Stata
Stata for the Behavioral Sciences
A Visual Guide to Stata Graphics, Third Edition
Microeconometrics Using Stata, Revised Edition
Thirty Years with Stata: A Retrospective
An Introduction to Survival Analysis Using Stata, Revised Third Edition
Multilevel and Longitudinal Modeling Using Stata, Third Edition

Interpreting and Visualizing Regression Models Using Stata

Michael Mitchell’s Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward.

Using a dataset based on the General Social Survey, Mitchell starts with a basic linear regression with a single independent variable and then illustrates how to tabulate and graph predicted values. Mitchell focuses on Stata’s margins and marginsplot commands, which play a central role in the book and which greatly simplify the calculation and presentation of results from regression models. In particular, through use of the marginsplot command, Mitchell shows how you can graphically visualize every model presented in the book. Gaining insight into results is much easier when you can view them in a graph rather than in a mundane table of results.

Mitchell then proceeds to more-complicated models where the effects of the independent variables are nonlinear. After discussing how to detect nonlinear effects, he presents examples using both standard polynomial terms (squares and cubes of variables) as well as fractional polynomial models, where independent variables can be raised to powers like −1 or 1/2. In all cases, Mitchell again uses the marginsplot command to illustrate the effect that changing an independent variable has on the dependent variable. Piecewise-linear models are presented as well; these are linear models in which the slope or intercept is allowed to change depending on the range of an independent variable. Mitchell also uses the contrast command when discussing categorical variables; as the name suggests, this command allows you to easily contrast predictions made for various levels of the categorical variable.

Interaction terms can be tricky to interpret, but Mitchell shows how graphs produced by marginsplot greatly clarify results. Individual chapters are devoted to two- and three-way interactions containing all continuous or all categorical variables and include many practical examples. Raw regression output including interactions of continuous and categorical variables can be nigh impossible to interpret, but again Mitchell makes this a snap through judicious use of the margins and marginsplot commands in subsequent chapters.

The first two-thirds of the book is devoted to cross-sectional data, while the final third considers longitudinal data and complex survey data. A significant difference between this book and most others on regression models is that Mitchell spends quite some time on fitting and visualizing discontinuous models—models where the outcome can change value suddenly at thresholds. Such models are natural in settings such as education and policy evaluation, where graduation or policy changes can make sudden changes in income or revenue.

This book is a worthwhile addition to the library of anyone involved in statistical consulting, teaching, or collaborative applied statistical environments. Graphs greatly aid the interpretation of regression models, and Mitchell’s book shows you how.

Financial Econometrics Using Stata

Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results.

After providing an intuitive introduction to time-series analysis and the ubiquitous autoregressive moving-average (ARMA) model, the authors carefully cover univariate and multivariate models for volatilities. Chapters on risk management and analyzing contagion show how to define, estimate, interpret, and perform inference on essential measures of risk and contagion.

The authors illustrate every topic with easily replicable Stata examples and explain how to interpret the results from these examples.

The authors have a unique blend of academic and industry training and experience. This training produced a practical and thorough approach to each of the addressed topics.

Stata for the Behavioral Sciences

Stata for the Behavioral Sciences, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to the reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.

Although this book is not designed as an introduction to Stata, it is appealing even to Stata novices. Throughout the text, Mitchell thoughtfully addresses any features of Stata that are important to understand for the analysis at hand. He also is careful to point out additional resources such as related videos from Stata's YouTube channel.

The book is divided into five sections.

The first section contains a chapter that introduces Stata commands for descriptive statistics and another that covers basic inferential statistics such as one- and two-sample t tests.

The second section focuses on between-subjects ANOVA modeling. The discussion moves from one-way ANOVA models to ANCOVA models to two-way and three-way ANOVA models. In each case, special attention is given to the use of commands such as contrast and margins for testing specific hypotheses of interest. Mitchell also emphasizes the understanding of interactions through contrasts and graphs. Underscoring the importance of planning any experiment, he discusses power analysis for t tests, for one- and two-way ANOVA models, and for ANCOVA models.

Section three of the book extends the discussion in the previous section to models for repeated-measures data and for longitudinal data.

The fourth section of the book illustrates the use of the regress command for fitting multiple regression models. Mitchell then turns his attention to tools for formatting regression output, for testing assumptions, and for model building. This section ends with a discussion of power analysis for simple, multiple, and nested regression models.

The final section has a tone that differs from the first four. Rather than focusing on a particular type of analysis, Mitchell describes elements of Stata. He first discusses estimation commands and similarities in syntax from command to command. Then, he details a set of postestimation commands that are available after most estimation commands. Another chapter provides an overview of data management commands. This section ends with a chapter that will be of particular interest to anyone who has used IBM® SPSS®; it lists commonly used SPSS® commands and provides equivalent Stata syntax.

This book is an easy-to-follow guide to analyzing data using Stata for researchers in the behavioral sciences and a valuable addition to the bookshelf of anyone interested in applying ANOVA methods to a variety of experimental designs.

Stata par la pratique : statistiques, graphiques et éléments de programmation

Stata par la pratique par Eric Cahuzac et Christophe Bontemps propose une introduction complète à l'usage de Stata en Français. S'appuyant sur des exemples clairs écrits dans un langage simple, cet ouvrage guide l'utilisateur au travers des différentes fonctionnalités de Stata 10. L'ensemble des outils nécessaires à un travail sur données est abordé : exploration des données, statistiques descriptives, modélisation, inférence, tests, graphiques, ainsi que les sorties pour publication. En outre, l'ouvrage inclut également une introduction à la programmation et propose des extraits de code utiles pour résoudre les problèmes fréquemment rencontrés par les utilisateurs. Il contient le matériel essentiel pour transformer le débutant en expert, la clarté de l'ouvrage rendant ce processus particulièrement rapide.

L'ouvrage propose un apprentissage de Stata par des approches variées. Pour certains sujets (statistiques descriptives par exemple), les commandes appropriées sont exposées et leur utilisation expliquée de manière simple. Si différents choix sont possibles, les avantages et inconvénients de chaque commande sont explicités afin de guider l'utilisateur dans ses choix. Pour les sujets plus complexes, l'approche est de type exploratoire : le lecteur est accompagné dans l'utilisation des différentes commandes pour analyser ou visualiser les données sur la base d'exemples choisis. Une autre originalité intéressante de ce livre réside dans le fait qu'il ne se limite pas aux commandes standard de Stata, mais présente aussi de nombreuses commandes additionnelles issues de la communauté des utilisateurs de Stata. Les exemples proposés sont principalement tirés de l'économie et des sciences sociales, mais sont illustratifs pour tout lecteur intéressé par une application statistique, quelle que soit sa spécialité. Cet ouvrage couvre un champ très large et s'adresse aux débutants comme aux utilisateurs confirmés, qu'ils soient étudiants ou chercheurs.

A Visual Guide to Stata Graphics, Third Edition

In its third edition, Michael Mitchell’s A Visual Guide to Stata Graphics remains the essential introduction and reference for Stata graphics. The third edition retains all the features that made the first two editions so useful:

A complete guide to Stata’s graph command and Graph Editor
Exhaustive examples of customized graphs using both command options and the Graph Editor
Visual indexing of features—just look for a picture that matches what you want to do

New in this edition are treatments of contour plots, margins plots, and font handling. Mitchell dedicates a new subsection to contour plots, showing you how to control the number of levels, how to change the colors used, and how to produce effective legends. Over 30 graphs are used to demonstrate what you can accomplish with the new marginsplot command—graphs of estimated means and marginal means (with confidence intervals), interaction graphs, comparisons of groups, and more. Mitchell also adds a section that shows you how to get bold text, italic text, subscripts, superscripts, and Greek letters into your titles, axes, labels, and other text.

The book retains its visual style, presenting the reader with a color-coded, visual table of contents that runs along the right edge of every page and shows readers exactly where they are in the book. You can see the color-coded chapter tabs without opening the book, providing quick visual access to each chapter.

The heart of each chapter is a series of entries that are typically formatted three to a page. Each entry shows a graph command (with the emphasized portion of the command highlighted in red), the resulting graph, a description of what is being done, the dataset and scheme used, and a section showing how to produce the result by using the Graph Editor. Because every feature, option, and edit is demonstrated with a graph or screen capture, you can often flip through a section of the book to find exactly the effect you are seeking.

The first chapter details how to use the book, the types of Stata graphs, how to use schemes to control the overall appearance of graphs, and how to use options to make specific modifications. It also outlines a process for building graphs with the graph command.

The second chapter is a complete overview of the Graph Editor. It includes over 120 color graphics and screen captures to show exactly how things are done and how they look on the graph. With pictures and words, Mitchell shows how to change the color, size, or placement of any titles, markers, annotations, or other objects on your graph by using just a few mouse clicks. More subtly, he shows how to change things such as the number of ticks and labels on your axes, the number of columns in your legends, the label on an individual point, and more. He even shows how to convert, for example, a scatterplot to a line plot and how to rotate or pivot bar charts. Mitchell also covers advanced topics such as how to draw lines and arrows on graphs so that they continue to reference your objects of interest even if you resize the graph, combine it with other graphs, or change the scale or range of the axes. In short, he exposes all the Graph Editor’s tools, from the simplest to the most powerful. Mitchell does not stop there; almost every example in the book shows you how to accomplish the desired graph or effect not only by using a command or command-line option but also by using the Graph Editor.

Of the Graph Editor, Mitchell writes,

[...] You need to use the Graph Editor for only a short amount of time to see what a smart and powerful tool it is. Whereas commands offer the power of repeatability, the Graph Editor provides a nimble interface that permits you to tangibly modify graphs like a potter directly handling clay.

In the third chapter, Mitchell discusses twoway graphs such as scatterplots, line plots, area plots, bar plots, range plots, contour plots, regression fits, and smooths. He shows how to create each of these types of graphs and how to use options (and the Graph Editor) to control how the graph looks. He also introduces graphing across groups of data and options for adding and controlling titles, notes, legends, and so forth. Beyond the basics, he shows how to easily overlay plots to obtain graphs such as regression fits with error contours and observed data scatters, local polynomial smooths with scatters of their underlying data, stock market–style graphs of open and closed values with quantities traded as a bar chart at the bottom, histograms with density smooths, and more. Because Stata’s graph command will let you customize any aspect of the graph, Mitchell spends ample time showing you the most valuable options for obtaining the look you want. If you are in a hurry to discover one special option, you can skim the chapter until you see the effect you want, and then glance at the command to see what is highlighted in red.

In the succeeding five chapters, Mitchell covers scatterplot matrices, bar graphs, box plots, dot plots, and pie charts. As with twoway graphs, he shows you how to create each of these graphs and how to adjust every aspect of the graph to your taste (or to a publisher’s required form).

In chapters 9 and 10, Mitchell undertakes an in-depth presentation of the options available across almost all graph types—options that add and change the look of titles, notes, and such; control the number of ticks on axes; control the content and appearance of the numbers and labels on axes; control legends; add and change the look of annotations; graph over subgroups; change the look of markers and their labels; apply schemes to control the look of the graph; change the look of graph regions; size graphs and their elements; and more. Again he shows how to make these changes both by using options and by using the Graph Editor.

To complete the graphical journey, Mitchell discusses and demonstrates the 12 styles that unite and control the appearance of the myriad graph objects. These styles are angles, colors, clock positions, compass directions, connecting points, line patterns, line widths, margins, marker sizes, orientations, marker symbols, and text sizes.

That completes the main body of the Visual Guide, but don’t skip the appendix. There, Mitchell first gives a quick overview of the dozens of statistical graph commands that are not strictly the subject of the book. Even so, these commands use the graph command as an engine to draw their graphs; therefore, almost all that Mitchell has discussed applies to them. To make this clear, he shows explicitly how to apply common options and common Graph Editor tools to statistical graphs. Then Mitchell takes you on a tour of the new marginsplot command. After that, he addresses combining graphs—showing you how to create complex and multipart images from previously created graphs.

In a crucial section entitled “Putting it all together”, Mitchell shows us how to do just that. We learn more about overlaying twoway plots, and we learn how to combine data management and graphics to create plots such as bar charts of rates with capped confidence intervals, scatterplots with range-finder confidence intervals in both dimensions, and population pyramids.

Mitchell then warns us about mistakes that can be made when typing graph commands and how to correct them. In the appendix, he even show us how to create our own scheme files. Scheme files allow you to control every aspect of how your graphs look without having to specify options. They are the answer to department or journal standards or if you just want all your graphs to have a common appearance different from the schemes shipped with Stata. As with the rest of the book, this section includes cross-references to the Stata Graphics Reference Manual to provide more depth on the subject. Finally, Mitchell reviews all datasets, schemes, and other online supplements available for the book.

The third edition of A Visual Guide to Stata Graphics is a complete guide to Stata’s graph command and the associated Graph Editor. Whether you want to tame the Stata graph command, quickly find out how to produce a graphical effect, master the Stata Graph Editor, or learn approaches that can be used to construct custom graphs, this is the book to read.

Microeconometrics Using Stata, Revised Edition

Microeconometrics Using Stata, Revised Edition, by A. Colin Cameron and Pravin K. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and up-to-date survey of microeconometric methods available in Stata.

The revised edition has been updated to reflect the new features available in Stata 11 germane to microeconomists. Instead of using mfx and the user-written margeff commands, the revised edition uses the new margins command, emphasizing both marginal effects at the means and average marginal effects. Factor variables, which allow you to specify indicator variables and interaction effects, replace the xi command. The new gmm command for generalized method of moments and nonlinear instrumental-variables estimation is presented, along with several examples. Finally, the chapter on maximum likelihood estimation incorporates the enhancements made to ml in Stata 11.

Early in the book, Cameron and Trivedi introduce simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. While simulation methods are important tools for econometricians, they are not covered in standard textbooks. By introducing simulation methods, the authors arm students and researchers with techniques they can use in future work. Cameron and Trivedi address each topic with an in-depth Stata example, and they reference their 2005 textbook, Microeconometrics: Methods and Applications, where appropriate.

The authors also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. Although the book is not specifically about Stata programming, it does show how to solve many programming problems. These techniques are essential in applied microeconometrics because there will always be new, specialized methods beyond what has already been incorporated into a software package.

Cameron and Trivedi’s choice of topics perfectly reflects the current practice of modern microeconometrics. After introducing the reader to Stata, the authors introduce linear regression, simulation, and generalized least-squares methods. The section on cross-sectional techniques is thorough, with up-to-date treatments of instrumental-variables methods for linear models and of quantile-regression methods.

The next section of the book covers estimators for the parameters of linear panel-data models. The authors’ choice of topics is unique: after addressing the standard random-effects and fixed-effects methods, the authors also describe mixed linear models—a method used in many areas outside of econometrics.

Cameron and Trivedi not only address methods for nonlinear regression models but also show how to code new nonlinear estimators in Stata. In addition to detailing nonlinear methods, which are omitted from most econometrics textbooks, this section shows researchers and students how to easily implement new nonlinear estimators.

The authors next describe inference using analytical and bootstrap approximations to the distribution of test statistics. This section highlights Stata’s power to easily obtain bootstrap approximations, and it also introduces the basic elements of statistical inference.

Cameron and Trivedi then include an extensive section about methods for different nonlinear models. They begin by detailing methods for binary dependent variables. This section is followed by sections about multinomial models, tobit and selection models, count-data models, and nonlinear panel-data models. Two appendices about Stata programming complete the book.

The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make Microeconometrics Using Stata an invaluable, hands-on addition to the library of anyone who uses microeconometric methods.

Thirty Years with Stata: A Retrospective

This volume is a sometimes serious and sometimes whimsical retrospective of Stata, its development, and its use over the last 30 years.

The view from the inside opens with an essay by Bill Gould, Stata's president and cofounder, that discusses the challenges and concepts that guided the design and implementation of Stata. This is followed by an interview of Bill by Joe Newton that discusses Bill's early interest in computing, his early work on a program for matching prom dates in the days when you had to purchase time on computers, and further exploration of the guiding principles behind Stata. Finally, Sean Becketti, Stata's first employee, delves into the not-to-be-missed culture of Stata in its infancy.

The view from the outside comprises 14 essays by prominent researchers and members of the Stata community. Most discuss Stata's use and evolution in disciplines such as behavioral sciences, business, economics, epidemiology, time series, political science, public health, public policy, veterinary epidemiology, and statistics. Some take a sweeping overview. Others are more intimate personal recollections.

Mostly, we simply wanted to celebrate the relationship between Stata users and Stata software. This volume holds something interesting for everyone.

An Introduction to Survival Analysis Using Stata, Revised Third Edition

An Introduction to Survival Analysis Using Stata, Revised Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines.

The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models.

Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement, Stata provides the stfamily of commands for organizing and summarizing survival data.

This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata's most widely used stcommands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata.

The first three chapters of the text cover basic theoretical concepts: hazard functions, cumulative hazard functions, and their interpretations; survivor functions; hazard models; and a comparison of nonparametric, semiparametric, and parametric methodologies. Chapter 4 deals with censoring and truncation. The next three chapters cover the formatting, manipulation, stsetting, and error checking involved in preparing survival data for analysis using Stata's st analysis commands. Chapter 8 covers nonparametric methods, including the Kaplan–Meier and Nelson–Aalen estimators and the various nonparametric tests for the equality of survival experience.

Chapters 9–11 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. The next four chapters cover parametric models, which are fit using Stata's streg command. These chapters include detailed derivations of all six parametric models currently supported in Stata and methods for determining which model is appropriate, as well as information on stratification, obtaining predictions, and advanced topics such as frailty models. Chapter 16 is devoted to power and sample-size calculations for survival studies. The final chapter covers survival analysis in the presence of competing risks.

Multilevel and Longitudinal Modeling Using Stata, Third Edition

Multilevel and Longitudinal Modeling Using Stata, Third Edition, by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at Stata’s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are “mixed” because they allow fixed and random effects, and they are “generalized” because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.

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