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.
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic, Postgraduate, and Professional Practice & Development
Maße
Höhe: 246 mm
Breite: 189 mm
Gewicht
ISBN-13
978-1-59718-359-8 (9781597183598)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Colin Cameron is a professor of economics at the University of California-Davis, where he teaches econometrics at undergraduate and graduate levels, as well as an undergraduate course in health economics. He has given short courses in Europe, Australia, Asia, and South America. His research interests are in microeconometrics, especially in robust inference for regression with clustered errors. He is currently an associate editor of the Stata Journal.
Pravin K. Trivedi is a Distinguished Professor Emeritus at Indiana University-Bloomington and an honorary professor in the School of Economics at the University of Queensland. During his academic career, he has taught undergraduate- and graduate-level econometrics in the United States, England, Europe, and Australia. His research interests include microeconometrics and health economics. He served as coeditor of the Econometrics Journal from 2000-2007 and associate editor of the Journal of Applied Econometrics from 1986-2015. He has coauthored (with David Zimmer) Copula Modeling in Econometrics: An Introduction for Practitioners (2007).
Cameron and Trivedi's joint work includes research articles on econometric models and tests for count data, the Econometric Society monograph Regression Analysis of Count Data, and the graduate-level text Microeconometrics: Methods and Applications.
Volume I. Stata basics. Data management and graphics. Linear regression basics. Linear regression extensions. Simulation. Linear regression with correlated errors. Linear instrumental-variables regression. Linear panel-data models: Basics. Linear panel-data models: Extensions. Introduction to nonlinear regression. Tests of hypotheses and model specification. Bootstrap methods. Nonlinear regression methods. Flexible regression: Finite mixtures and nonparametric. Quantile regression. Programming in Stata. Mata. Optimization in Mata. Volume II. Nonlinear optimization methods. Binary outcome models. Multinomial models. Tobit and selection models. Count-data models. Survival analysis for duration data. Nonlinear panel models. Parametric models for heterogeneity and endogeneity. Randomized control trials and exogenous treatment effects. Endogenous treatment effects. Spatial regression. Semiparametric regression. Machine learning for prediction and inference. Bayesian methods: Basics. Bayesian methods: Markov chain Monte Carlo algorithms
