This outstanding introduction to microeconometrics research using Stata offers the most complete and up-to-date survey of methods available. The authors address each topic with an in-depth example and demonstrate how to use Stata's programming features to implement methods for which the application does not have a specific command.
Covering important topics omitted from basic introductions to Stata, Microeconometrics Using Statashows how to do microeconometric research using Stata. It provides the most complete and up-to-date survey of microeconometric methods available in Stata. After a brief introduction to Stata, the authors present linear regression, simulation, and generalized least squares methods. The section on cross-sectional techniques is complete with up-to-date treatments of instrumental-variables methods for linear models as well as quantile regression methods. The next section covers estimators for the parameters of linear panel-data models. The book explores standard random-effects and fixed-effects methods, along with mixed linear models used in many areas outside of econometrics. After introducing methods for nonlinear regression models, the authors discuss how to code new, nonlinear estimators in Stata. They show how to easily implement new nonlinear estimators. The authors also cover inference using analytical and bootstrap approximations to the distribution of test statistics. The book then contains a section on methods for different nonlinear models, including multinomial, selection, count-data, and nonlinear panel-data models. By combining intuitive introductions and detailed discussions of Stata examples, this bookprovides an invaluable hands-on introduction to microeconometrics.
The goal of the book is to make easier to carry out the computations necessary for the full interpretation of regression nonlinear models for categorical outcomes usign 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 applied meaning of interactions in nonlinear models such as logistic regression. The tools in Mitchell's book make this task much more enjoyable and comprehensible. Mitchell starts with simple linear regression (which is simple in all ways), and then adds polynomials and discontinuities. This is followed by 2-way and 3-way interaction until interpretation of coefficients through words is difficult. By careful use of Stata's marginsplot command, Mitchell shows how well graphs can be used to show effects. He also includes careful verbal interpretation of coefficients to make communications complete. He then extends the methods from linear regression to various types of nonlinear regression, such as multilevel or survival models. 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.
Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized in four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.
"Multilevel and Longitudinal Modeling Using Stata, Fourth Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels. Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. Across volumes, the 16 chapters, over 140 exercises, and over 110 datasets span a wide range of disciplines, making the book suitable for courses in the medical, social, and behavioral sciences and in applied statistics. This first volume is dedicated to models for continuous responses and is a prerequisite for the second volume on models for other response types. It has been thoroughly revised and updated for Stata 16. New material includes the Kenward-Roger degree-of-freedom correction for improved inference with a small number of clusters, difference-in-differences estimation for natural experiments, and instrumental-variable estimation to handle level-1 endogeneity"--
Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.