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 INTRODUCTION AND BACKGROUND 1 Introduction 2 Conditional Expectations and Related Concepts in Econometrics 3 Basic Asymptotic Theory II LINEAR MODELS 4 Single-Equation Linear Model and Ordinary Least Squares Estimation 5 Instrumental Variables Estimation of Single-Equation Linear Models 6 Additional Single-Equation Topics 7 Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares 8 System Estimation by Instrumental Variables 9 Simultaneous Equations Models 10 Basic Linear Unobserved E¤ects Panel Data Models 11 More Topics in Linear Unobserved Effects Models III GENERAL APPROACHES TO NONLINEAR ESTIMATION 12 M-Estimation, Nonlinear Regression, and Quantile Regression 13 Maximum Likelihood Methods 14 Generalized Method of Moments and Minimum Distance Estimation IV NONLINEAR MODELS AND RELATED TOPICS 15 Binary Response Models 16 Multinomial and Ordered Response Models 17 Corner Solution Responses 18 Count, Fractional, and Other Nonnegative Responses 19 Censored Data, Sample Selection, and Attrition 20 Stratified Sampling and Cluster Sampling 21 Estimating Average Treatment Effects 22 Duration Analysis References Index