Microeconometrics Using Stata, Second Edition
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Comment from the Stata technical group
Every applied economic researcher using Stata and everyone teaching or studying microeconometrics will benefit from Cameron and Trivedi's two volumes. They are an invaluable reference of the theory and intuition behind microeconometric methods using Stata. Those familiar with Cameron and Trivedi's Microeconometrics: Methods and Applications will find the same rigor. Those familiar with the previous edition of "Microeconometrics Using Stata" will find the familiar focus on Stata commands, their interpretation, and their connection with microeconometric theory as well as an introduction to computational concepts that should be part of any researcher's toolbox. And readers will find much more—so much more the second edition required a second volume.
This new edition covers all the new Stata developments relevant to microeconometrics that appeared since the the last edition in 2010. For example, readers will find entire new chapters on treatment effects, duration models, spatial autoregressive models, lasso, and Bayesian analysis. But the authors didn't stop there. They also added discussions of the most recent microeconometric methods that have been contributed by the Stata community.
The first volume introduces foundational microeconometric methods, including linear and nonlinear methods for cross-sectional data and linear panel data with and without endogeneity as well as overviews of hypothesis and model-specification tests. Beyond this, it teaches bootstrap and simulation methods, quantile regression, finite mixture models, and nonparametric regression. It also includes an introduction to basic Stata concepts and programming and to Mata for matrix programming and basic optimization.
The second volume builds on methods introduced in the first volume and walks readers through a wide range of more advanced methods useful in economic research. It starts with an introduction to nonlinear optimization methods and then delves into binary outcome methods with and without endogeneity; tobit and selection model estimates with and without endogeneity; choice model estimation; count data with and without endogeneity for conditional means and count data for conditional quantiles; survival data; nonlinear panel-data methods with and without endogeneity; exogenous and endogenous treatment effects; spatial data modeling; semiparametric regression; lasso for prediction and inference; and Bayesian econometrics.
With its encyclopedic coverage of modern econometric methods paired with many worked examples that demonstrate how to implement these methods in Stata, "Microeconometrics Using Stata, Second Edition" is a text that readers will come back to over and over for each new project or analysis they face. It is an essential reference for applied researchers and those taking microeconometrics courses.
About the authors
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 has 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.
Table of contents
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