[stata资源分享]最新Stata Press书籍汇总（I）
推荐：限时优惠 | 2017年8月Stata暑期特训课程火热招生中
目录
Stata Press books are listed alphabetically by author.
Discovering Structural Equation Modeling Using Stata, Revised Edition
Data Analysis Using Stata, Third Edition
A Gentle Introduction to Stata, Fifth Edition
Regression Models for Categorical Dependent Variables Using Stata, Third Edition
An Introduction to Modern Econometrics Using Stata
The Workflow of Data Analysis Using Stata
Data Management Using Stata: A Practical Handbook
Introduction to Time Series Using Stata
An Introduction to Stata Programming, Second Edition
Discovering Structural Equation Modeling Using Stata, Revised Edition
书籍介绍：Discovering Structural Equation Modeling Using Stata, Revised Edition, by Alan Acock, successfully introduces both the statistical principles involved in structural equation modeling (SEM) and the use of Stata to fit these models. The book uses an application-based approach to teaching SEM. Acock demonstrates how to fit a wide variety of models that fall within the SEM framework and provides datasets that enable the reader to follow along with each example. As each type of model is discussed, concepts such as identification, handling of missing data, model evaluation, and interpretation are covered in detail.
In Stata, structural equation models can be fit using the command language or the graphical user interface (GUI) for SEM, known as the SEM Builder. The book demonstrates both of these approaches. Throughout the text, the examples use the sem command. Each chapter also includes brief discussions on drawing the appropriate path diagram and performing estimation from within the SEM Builder. A more in-depth coverage of the SEM Builder is given in one of the book’s appendixes.
The first two chapters introduce the building blocks of SEM. Chapter 1 begins with overviews of Cronbach’s alpha as a measure of reliability and of exploratory factor analysis. Then, building on these concepts, Acock demonstrates how to perform confirmatory factor analysis, discusses a variety of statistics available for assessing the fit of the model, and shows a more general measurement of reliability that is based on confirmatory factor analysis. Chapter 2 focuses on using SEM to perform path analysis. It includes examples of mediation, moderation, cross-lagged panel models, and nonrecursive models.
Chapter 3 demonstrates how to combine the topics covered in the first two chapters to fit full structural equation models. The use of modification indices to guide model modification and computation of direct, indirect, and total effects for full structural equation models are also covered.
Chapter 4 details the application of SEM to growth curve modeling. After introducing the basic linear latent growth curve model, Acock extends this to more complex cases such as the inclusion of quadratic terms, time-varying covariates, and time-invariant covariates.
In chapter 5, Acock discusses testing for differences across groups in SEM. He introduces the specialized sem syntax for multiple-group models and discusses the intricacies of testing for group differences for the different types of models presented in the preceding chapters.
The Revised Edition includes output, syntax, and instructions for fitting models with the SEM Builder that have been updated for Stata 13.
Discovering Structural Equation Modeling Using Stata, Revised Edition is an excellent resource both for those who are new to SEM and for those who are familiar with SEM but new to fitting these models in Stata. It is useful as a text for courses covering SEM as well as for researchers performing SEM.
书籍介绍：Data Analysis Using Stata, Third Edition has been completely revamped to reflect the capabilities of Stata 12. This book will appeal to those just learning statistics and Stata, as well as to the many users who are switching to Stata from other packages. Throughout the book, Kohler and Kreuter show examples using data from the German Socio-Economic Panel, a large survey of households containing demographic, income, employment, and other key information.
Kohler and Kreuter take a hands-on approach, first showing how to use Stata’s graphical interface and then describing Stata’s syntax. The core of the book covers all aspects of social science research, including data manipulation, production of tables and graphs, linear regression analysis, and logistic modeling. The authors describe Stata’s handling of categorical covariates and show how the new margins and marginsplot commands greatly simplify the interpretation of regression and logistic results. An entirely new chapter discusses aspects of statistical inference, including random samples, complex survey samples, nonresponse, and causal inference.
The rest of the book includes chapters on reading text files into Stata, writing programs and do-files, and using Internet resources such as the searchcommand and the SSC archive.
Data Analysis Using Stata, Third Edition has been structured so that it can be used as a self-study course or as a textbook in an introductory data analysis or statistics course. It will appeal to students and academic researchers in all the social sciences.
A Gentle Introduction to Stata, Fifth Edition
书籍介绍：Alan C. Acock's A Gentle Introduction to Stata, Fifth Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able not only to use Stata well but also to learn new aspects of Stata.
Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book.
Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and structural equation modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material.
The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward-referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material in a natural fashion. Real datasets, such as the General Social Surveys from 2002 and 2006, are used throughout the book.
The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements.
The fifth edition of the book includes two new chapters that cover multilevel modeling and item response theory (IRT) models. The multilevel modeling chapter demonstrates how to fit linear multilevel models using the mixed command. Acock discusses models with both random intercepts and random coefficients, and he provides a variety of examples that apply these models to longitudinal data. The IRT chapter introduces the use of IRT models for evaluating a set of items designed to measure a specific trait such as an attitude, a value, or a belief. Acock shows how to use the irt suite of commands, which are new in Stata 14, to fit IRT models and to graph the results. In addition, he presents a measure of reliability that can be computed when using IRT.
Regression Models for Categorical Dependent Variables Using Stata, Third Edition
书籍介绍：Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void.
The third edition is divided into two parts. Part I begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fitting, and interpretation of models for categorical dependent variables. The book is thus accessible to new users of Stata and those who are new to categorical data analysis. Part II is devoted to a comprehensive treatment of estimation and interpretation for binary, ordinal, nominal, and count outcomes.
Readers familiar with previous editions will find many changes in the third edition. An entire chapter is now devoted to interpretation of regression models using predictions. This concept is explored in greater depth in Part II. The authors also discuss how many improvements made to Stata in recent years—factor variables, marginal effects with margins, plotting predictions using marginsplot—facilitate analysis of categorical data.
The authors advocate a variety of new methods that use predictions to interpret the effect of variables in regression models. Readers will find all discussion of statistical concepts firmly grounded in concrete examples. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata.
Examples in the new edition also illustrate changes to the authors' popular SPost commands after a recent rewrite inspired by the authors' evolving views on interpretation. Readers will note that SPost now takes full advantage of the power of the margins command and the flexibility of factor-variable notation. Long and Freese also provide a suite of new commands, including mchange, mtable, and mgen. These commands complement margins, aiding model interpretation, hypothesis testing, and model diagnostics. They offer the same syntactical convenience features that users of Stata expect, for example including powers or interactions of covariates in regression models and seamlessly working with complex survey data. The authors also discuss how to use these commands to estimate marginal effects, either averaged over the sample or evaluated at fixed values of the regressors.
The third edition of Regression Models for Categorical Dependent Variables Using Stata continues to provide the same high-quality, practical tutorials of previous editions. It also offers significant improvements over previous editions—new content, updated information about Stata, and updates to the authors' own commands. This book should be on the bookshelf of every applied researcher analyzing categorical data and is an invaluable learning resource for students and others who are new to categorical data analysis.
An Introduction to Modern Econometrics Using Stata
书籍介绍：An Introduction to Modern Econometrics Using Stata, by Christopher F. Baum, successfully bridges the gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.
The first three chapters are dedicated to the basic skills needed to effectively use Stata: loading data into Stata; using commands like generate andreplace, egen, and sort to manipulate variables; taking advantage of loops to automate tasks; and creating new datasets by using merge and append. Baum succinctly yet thoroughly covers the elements of Stata that a user must learn to become proficient, providing many examples along the way.
Chapter 4 begins the core econometric material of the book and covers the multiple linear regression model, including efficiency of the ordinary least-squares estimator, interpreting the output from regress, and point and interval prediction. The chapter covers both linear and nonlinear Wald tests, as well as constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models.
Chapters 5 and 6 focus on consequences of failures of the linear regression model’s assumptions. Chapter 5 addresses topics like omitted-variable bias, misspecification of functional form, and outlier detection. Chapter 6 is dedicated to non-independently and identically distributed errors, and it introduces the Newey–West and Huber/White covariance matrices, as well as feasible generalized least-squares estimation in the presence of heteroskedasticity or serial correlation. Chapter 7 is dedicated to the use of indicator variables and interaction effects.
Instrumental-variables estimation has been an active area of research in econometrics, and chapter 8 commendably addresses issues like weak instruments, underidentification, and generalized method-of-moments estimation. In this chapter, Baum extensively uses his wildly popular ivreg2 command.
The last two chapters briefly introduce panel-data analysis and discrete and limited-dependent variables. Two appendices detail importing data into Stata and Stata programming. As in all chapters, Baum presents many Stata examples.
An Introduction to Modern Econometrics Using Stata can serve as a supplementary text in both undergraduate- and graduate-level econometrics courses, and the book’s examples will help students quickly become proficient in Stata. The book is also useful to economists and businesspeople wanting to learn Stata by using practical examples.
The Workflow of Data Analysis Using Stata
书籍介绍：The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Aimed at anyone who analyzes data, this book presents an effective strategy for designing and doing data-analytic projects.
In this book, Long presents lessons gained from his experience with numerous academic publications, as a coauthor of the immensely popular Regression Models for Categorical Dependent Variables Using Stata, and as a coauthor of the SPOST routines, which are downloaded over 20,000 times a year.
A workflow of data analysis is a process for managing all aspects of data analysis. Planning, documenting, and organizing your work; cleaning the data; creating, renaming, and verifying variables; performing and presenting statistical analyses; producing replicable results; and archiving what you have done are all integral parts of your workflow.
Long shows how to design and implement efficient workflows for both one-person projects and team projects. Long guides you toward streamlining your workflow, because a good workflow is essential for replicating your work, and replication is essential for good science.
An efficient workflow reduces the time you spend doing data management and lets you produce datasets that are easier to analyze. When you methodically clean your data and carefully choose names and effective labels for your variables, the time you spend doing statistical and graphical analyses will be more productive and more enjoyable.
After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. Long presents conventions that greatly simplify data analysis—conventions for naming, labeling, documenting, and verifying variables. He also covers cleaning, analyzing, and protecting your data.
While describing effective workflows, Long also introduces the concepts of basic data management using Stata and writing Stata do-files. Using real-world examples, Stata commands, and Stata scripts, Long illustrates effective techniques for managing your data and analyses. If you analyze data, this book is recommended for you.
7
An Introduction to Stata Programming, Second Edition
书籍介绍：Christopher F. Baum's An Introduction to Stata Programming, Second Edition, is a great reference for anyone who wants to learn Stata programming.
Baum assumes readers have some familiarity with Stata, but readers who are new to programming will find the book accessible. He begins by introducing programming concepts and basic tools. More advanced programming tools such as structures and pointers and likelihood-function evaluators using Mata are gradually introduced throughout the book alongside examples.
This new edition reflects some of the most important statistical tools added since Stata 10. 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.
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 command to calculate percentiles and the range of a variable, complete with documentation and certification.
After introducing the fundamentals of command development, Baum shows users how these concepts can be applied to help them write their own custom estimation commands by using 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. Baum briefly discusses how ado-file programming concepts relate to Mata functions and objects. He also explains some of the advantages of using Mata for certain programming tasks. Baum introduces concepts by providing the background and importance of 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 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.
Data Management Using Stata: A Practical Handbook
书籍介绍：Michael N. Mitchell’s Data Management Using Stata comprehensively covers data-management tasks, from those a beginning statistician would need to those hard-to-verbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.
The book is modular in structure, with modules based on data-management tasks rather than on clusters of commands. This format is helpful because it allows readers to find just what they need to solve a problem at hand. To complement this format, the book is in a style that will teach even sporadic readers good habits in data management, even if the reader chooses to read chapters out of order.
Throughout the book, Mitchell subtly emphasizes the absolute necessity of reproducibility and an audit trail. Instead of stressing programming esoterica, Mitchell reinforces simple habits and points out the time-savings gained by being careful. Mitchell’s experience in UCLA’s Academic Technology Services clearly drives much of his advice.
Mitchell includes advice for those who would like to learn to write their own data-management Stata commands. Even experienced users will learn new tricks and new ways to approach data-management problems.
This is a great book—thoroughly recommended for anyone interested in data management using Stata.
Introduction to Time Series Using Stata
书籍介绍：Introduction to Time Series Using Stata, by Sean Becketti, provides a practical guide to working with time-series data using Stata and will appeal to a broad range of users. The many examples, concise explanations that focus on intuition, and useful tips based on the author’s decades of experience using time-series methods make the book insightful not just for academic users but also for practitioners in industry and government.
The book is appropriate both for new Stata users and for experienced users who are new to time-series analysis.
Chapter 1 provides a mild yet fast-paced introduction to Stata, highlighting all the features a user needs to know to get started using Stata for time-series analysis. Chapter 2 is a quick refresher on regression and hypothesis testing, and it defines key concepts such as white noise, autocorrelation, and lag operators.
Chapter 3 begins the discussion of time series, using moving-average and Holt–Winters techniques to smooth and forecast the data. Becketti also introduces the concepts of trends, cyclicality, and seasonality and shows how they can be extracted from a series. Chapter 4 focuses on using these methods for forecasting and illustrates how the assumptions regarding trends and cycles underlying the various moving-average and Holt–Winters techniques affect the forecasts produced. Although these techniques are sometimes neglected in other time-series books, they are easy to implement, can be applied to many series quickly, often produce forecasts just as good as more complicated techniques, and as Becketti emphasizes, have the distinct advantage of being easily explained to colleagues and policy makers without backgrounds in statistics.
Chapters 5 through 8 encompass single-equation time-series models. Chapter 5 focuses on regression analysis in the presence of autocorrelated disturbances and details various approaches that can be used when all the regressors are strictly exogenous but the errors are autocorrelated, when the set of regressors includes a lagged dependent variable and independent errors, and when the set of regressors includes a lagged dependent variable and autocorrelated errors. Chapter 6 describes the ARIMA model and Box–Jenkins methodology, and chapter 7 applies those techniques to develop an ARIMA-based model of U.S. GDP. Chapter 7 in particular will appeal to practitioners because it goes step by step through a real-world example: here is my series, now how do I fit an ARIMA model to it? Chapter 8 is a self-contained summary of ARCH/GARCH modeling.
In the final portion of the book, Becketti discusses multiple-equation models, particularly VARs and VECs. Chapter 9 focuses on VAR models and illustrates all key concepts, including model specification, Granger causality, impulse-response analyses, and forecasting, using a simple model of the U.S. economy; structural VAR models are illustrated by imposing a Taylor rule on interest rates. Chapter 10 presents nonstationary time-series analysis. After describing nonstationarity and unit-root tests, Becketti masterfully navigates the reader through the often-confusing task of specifying a VEC model, using an example based on construction wages in Washington, DC, and surrounding states. Chapter 11 concludes.
Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. He was a developer of Stata in its infancy, and he was Editor of the Stata Technical Bulletin, the precursor to the Stata Journal, between 1993 and 1996. He has been a regular Stata user since its inception, and he wrote many of the first time-series commands in Stata.
Introduction to Time Series Using Stata, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. It can serve as both a reference for practitioners and a supplemental textbook for students in applied statistics courses.
限时优惠 | 2017年8月Stata暑期特训课程火热招生中
l 计量经济学服务中心 l
更懂计量，更懂你
回复关键词：微信群，进入中心科研社群
学术QQ群：593977756（500人大群）
品牌网站：www.aiwanning.com