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Econometric Analysis of Cross Section and Panel Data, Second Edition

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Author:
Jeffrey M. Wooldridge
Publisher: MIT Press
Copyright: 2010
ISBN-13: 978-0-262-23258-6
Pages: 1,096; hardcover
Price: $84.50

Comment from the Stata technical group

The second edition of Econometric Analysis of Cross Section and Panel Data, by Jeffrey Wooldridge, is invaluable to students and practitioners alike, and it should be on the shelf of all students and practitioners who are interested in microeconometrics.

This book is more focused than some other books on microeconometrics. It delves more deeply into the intuition and the theory underlying the covered techniques. The theoretical discussions can be understood by students, practitioners, and theoreticians. This book does not provide detailed coverage of simulation-based estimation techniques, resampling methods for estimating the distributions of estimators and test statistics, or nonparametric methods. The author’s focused approach leads to outstanding treatments of the covered topics.

Wooldridge’s book provides an impressive introduction to state-of-the-art methods for solving real-world problems in econometrics, including instructive examples and applied problems. In particular, the author approaches problems by applying the analogy principle and a general estimation method, by relying on the assumption that right-hand side variables are always random covariates, by paying close attention to the sampling design, and by treating interpretation as vital to the process.

This textbook provides outstanding coverage of sampling design, the use of survey weights for estimation and inference, the generalized method of moments (GMM) approach to panel data, and the related issues of sample selection, stratified sampling, and attrition in panel data.

The author’s list of additions to the second edition is four pages long. Among the important additions are a more complete discussion of the Mundlak–Chamberlain approach to linear and nonlinear panel-data estimators, more thorough discussions of the control function approach to models with endogenous variables, estimators for many more nonlinear models, and a thorough rewrite of the chapter on estimating average treatment effects to reflect the latest research.

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