Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods. Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata. The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.

Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods. Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata. The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.

Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods. Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata. The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than twenty-five years. In this book, they combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics, and quantitative social sciences. The book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors' homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than one hundred new references added. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.