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书单|量化研究学习的7本教材 从基础到精通（stata版）

[stata资源分享]最新Stata Press书籍汇总（I）

Speaking Stata Graphics

Speaking Stata Graphics is ideal for researchers who want to produce effective, publication-quality graphs. A compilation of articles from the popular “Speaking Stata” column by Nicholas J. Cox, this book provides valuable insights about Stata's built-in and user-written statistical-graphics commands.

Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model

Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model is concerned with obtaining a compromise between Cox and parametric models that retains the desired features of both types of models. The book is aimed at researchers who are familiar with the basic concepts of survival analysis and with the stcox and streg commands in Stata. As such, it is an excellent complement to An Introduction to Survival Analysis Using Stata by Cleves, Gould, and Marchenko.

One Hundred Nineteen Stata Tips, Third Edition

One Hundred Nineteen Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003.

Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition

 since 2003.

Meta-analysis allows researchers to combine results of several studies into a unified analysis that provides an overall estimate of the effect of interest and to quantify the uncertainty of that estimate. Stata has some of the best statistical tools available for doing meta-analysis. The unusual thing about these tools is that none of them are part of official Stata. They are all created by and documented by experts in the broader research community who also happen to be proficient Stata developers.

Maximum Likelihood Estimation with Stata, Fourth Edition

Maximum Likelihood Estimation with Stata, Fourth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s ml command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.

The book shows you how to take full advantage of the ml command’s noteworthy features:

· linear constraints

· four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH)

· observed information matrix (OIM) variance estimator

· outer product of gradients (OPG) variance estimator

· Huber/White/sandwich robust variance estimator

· cluster–robust variance estimator

· complete and automatic support for survey data analysis

· direct support of evaluator functions written in Mata

When appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.

The fourth edition has been updated to include new features introduced in recent versions of Stata. Such features include new methods for handling scores, more consistent arguments for likelihood-evaluator programs, and support for likelihood evaluators written in Mata (Stata’s matrix programming language). The authors illustrate how to write your estimation command so that it fully supports factor-variable notation and the svy prefix for estimation with survey data. They have also restructured the chapters that introduce ml in a way that allows you to begin working with ml faster. This edition is essential for anyone using Stata 11.

In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression.

The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands—commands that look and behave just like the official estimation commands in Stata.

Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.

Probabilité et Statistique pour les Sciences de la Santé: Apprentissage au Moyen du Logiciel Stata

Probabilité et Statistique pour les Sciences de la Santé: Apprentissage au Moyen du Logiciel Stata, par Patrick Taffé, se veut un livre différent de nombreux ouvrages théoriques traitant des probabilités et de la statistique. Cet ouvrage (en français) non seulement présente, de façon rigoureuse, les concepts et méthodes statistiques, mais aussi utilise des exemples concrets pour illustrer chaque concept théorique nouvellement introduit. Le lecteur va apprendre à réaliser des analyses au moyen de Stata, basé sur des vraies données. De nombreuses illustrations et nombreux exemples d'applications sont donnés pour apprendre au lecteur à mettre en pratique les techniques d'analyse. Enfin, des exercices à réaliser avec Stata et impliquant le plus souvent un petit jeu de données, sont proposés à la fin de chaque section afin de mettre en oeuvre les connaissances nouvellement acquises.

Generalized Linear Models and Extensions, Third Edition

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as GLMs with Stata’s glm command offers some advantages. For example, model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution.

Bayesian Analysis with Stata

Bayesian Analysis with Stata is a compendium of Stata user-written commands for Bayesian analysis. It contains just enough theoretical and foundational material to be useful to all levels of users interested in Bayesian statistics, from neophytes to aficionados.

The book is careful to introduce concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves. Bayesian Analysis with Stata is wonderful because it goes through the computational methods three times—first using Stata's ado-code, then using Mata, and finally using Stata to run the MCMC chains with WinBUGS or OpenBUGS. This reinforces the material while making all three methods accessible and clear. Once the book explains the computations and underlying methods, it satisfies the user's yearning for more complex models by providing examples and advice on how to implement such models. The book covers advanced topics while showing the basics of Bayesian analysis—which is quite an achievement.

Bayesian Analysis with Stata

The book is based on the assumption that the reader has some basic knowledge of statistics but no knowledge of Stata. The authors build the reader's abilities as a builder would build a house: laying a firm foundation in Stata, framing a general structure in which good work can be accomplished, adding the details that are particular to various types of statistical analyses, and, finally, trimming with a thorough treatment of graphics and special topics such as power and sample-size computations.

Aplicaciones en Economía y Ciencias Sociales con Stata

Aplicaciones en Economía y Ciencias Sociales con Stata es la primera publicación en español de Stata Press. El contenido ha sido el resultado de un trabajo que reúne a diversos autores en diferentes áreas de conocimiento y que muestran el uso de una variedad de herramientas de análisis disponibles en Stata. Cada uno de los capítulos presenta el desarrollo de una investigación particular donde se analiza un tópico específico y se emplean técnicas estadísticas y econométricas para sustentar las conclusiones con resultados empíricos que pueden ser en su mayoría reproducidos con datos y do-files disponibles en la página web del libro. Se intenta de esta manera que el lector tenga acceso directo a la metodología empleada por cada uno de los autores.

限时优惠 | 2017年8月Stata暑期特训课程火热招生中

学术研究利器你学会了多少？EViews计量经济学应用培训

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