Econometric Analysis of Cross Section and Panel Data
The second edition of this acclaimed graduate text provides a
unified treatment of the analysis of two kinds of data structures
used in contemporary econometric research: cross section data and
panel data. The book covers both linear and nonlinear models,
including models with dynamics and/or individual heterogeneity. In
addition to general estimation frameworks (particularly methods of
moments and maximum likelihood), specific linear and nonlinear
methods are covered in detail, including probit and logit models,
multinomial and ordered choice models, Tobit models and two-part
extensions, models for count data, various censored and missing
data schemes, causal (or treatment) effect estimation, and duration
analysis. Control function and correlated random effects approaches
are expanded to allow estimation of complicated models in the
presence of endogeneity and heterogeneity. This second edition has
been substantially updated and revised. Improvements include a
broader class of models for missing data problems; more detailed
treatment of cluster sampling problems, an important topic for
empirical researchers and much more
The second edition of a comprehensive state-of-the-art graduate
level text on microeconometric methods, substantially revised and
updated.
This book is delayed from its originally announced spring 2007
release. Backorders are being accepted and will be fulfilled upon
publication. Check this Web page for updates to the month of
publication. Publication in European markets will be approximately
one month later than the indicated American publication date.
The second edition of this acclaimed graduate text provides a
unified treatment of two methods used in contemporary econometric
research, cross section and data panel methods. By focusing on
assumptions that can be given behavioral content, the book
maintains an appropriate level of rigor while emphasizing intuitive
thinking. The analysis covers both linear and nonlinear models,
including models with dynamics and/or individual heterogeneity. In
addition to general estimation frameworks (particular methods of
moments and maximum likelihood), specific linear and nonlinear
methods are covered in detail, including probit and logit models
and their multivariate, Tobit models, models for count data,
censored and missing data schemes, causal (or treatment) effects,
and duration analysis.
Econometric Analysis of Cross Section and Panel Data was the first
graduate econometrics text to focus on microeconomic data
structures, allowing assumptions to be separated into population
and sampling assumptions. This second edition has been
substantially updated and revised. Improvements include a broader
class of models for missing data problems; more detailed treatment
of cluster problems, an important topic for empirical researchers;
expanded discussion of 'generalized instrumental variables'
(GIV) estimation; new coverage (based on the author's own
recent research) of inverse probability weighting; a more complete
framework for estimating treatment effects with panel data, and a
firmly established link between econometric approaches to nonlinear
panel data and the 'generalized estimating equation'
literature popular in statistics and other fields. New attention is
given to explaining when particular econometric methods can be
applied; the goal is not only to tell readers what does work, but
why certain 'obvious' procedures do not. The numerous
included exercises, both theoretical and computer-based, allow the
reader to extend methods covered in the text and discover new
insights.
"I highly recommend this book for graduate classes in econometrics. We have used it at MIT and the students find it extremely helpful. Wooldridge covers topics in a highly readable and insightful way." ¿Jerry Hausman, John and Jennie S. MacDonald Professor of Economics, MIT
Jeffrey M. Wooldridge is University Distinguished Professor of Economics at Michigan State University and a Fellow of the Econometric Society.
