The second edition of a comprehensive state-of-the-art graduate level text on microeconometric
methods substantially revised and updated. 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.
