Econometric analysis of cross section and panel data - Jeffrey M Wooldridge

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.