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Econometric Analysis, Seventh Edition

$194.50 each

Author:
William H. Greene
Publisher: Prentice Hall
Copyright: 2012
ISBN-13: 978-0-13-139538-1
Pages: 1,188; hardcover
Price: $194.50
Supplements:Author's website

Comment from the Stata technical group

William Greene’s Econometric Analysis has served as the standard reference for econometrics among economists, political scientists, and other social scientists for two decades. The newly released seventh edition is certain to carry on that tradition. The book’s abundance of examples and Greene’s emphasis on how to put econometric theory to practical use make the book valuable not only to graduate students taking their first course in econometrics but also to students and professionals who engage in empirical research.

As with most econometrics texts, the book begins by introducing the linear regression model. Part I of the book, consisting of eight chapters, begins with properties of the least-squares estimator; inference and prediction; and tests for functional form and specification. Chapter 7 covers nonlinear models, including a new discussion of interaction effects. Part I ends with a revised Chapter 8 that covers instrumental variables and endogeneity.

Part II of the book generalizes the linear regression model to allow for heteroskedasticity. Then, with the generalized least-squares (GLS) estimator already discussed in the context of nonspherical disturbances, Greene presents fixed- and random-effects panel-data models as straightforward extensions of least squares. Another chapter applies GLS to systems of regression equations.

Part III devotes one chapter to each of four popular estimation methods: the generalized method of moments, maximum likelihood, simulation, and Bayesian inference. Each chapter strikes a good balance between theoretical rigor and applications. Many newer discrete-choice models require evaluation of multivariate normal probabilities; thus, Chapter 15 includes a detailed discussion of the GHK simulator. New in Chapter 15 is an expanded treatment of the bootstrap.

Part IV covers advanced techniques for microeconometrics. Chapter 17 details binary choice models for both cross-sectional and panel data; a new subsection shows how to account for attrition by using inverse probability weighting. Also included in Part IV are bivariate and multivariate probit models; models for count, multinomial, and ordered outcomes; and models for truncated data, duration data, and sample selection. Part IV ends with a section on treatment effects, propensity-score matching, and regression discontinuity.

Part V covers advanced techniques for macroeconometrics. Chapter 20 on stationary time series describes estimation in the presence of serial correlation, tests for autocorrelation, lagged dependent variables, and ARCH models. Chapter 21 on nonstationary series covers unit roots and cointegration. The chapters in Part V frequently use the results obtained in Part III on estimation. The book concludes with appendices on matrix algebra, probability, distribution theory, and optimization.

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