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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; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage of inverse probability weighting; a more complete framework for estimating treatment effects with assumptions concerning the intervention and different data structures, including 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

1 Introduction 3
2 Conditional Expectations and Related Concepts in Econometrics 13
3 Basic Asymptotic Theory 37

II LINEAR MODELS 51

4 Single-Equation Linear Model and Ordinary Least Squares Estimation 53
5 Instrumental Variables Estimation of Single-Equation Linear Models 89
6 Additional Single-Equation Topics 123
7 Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares 161
8 System Estimation by Instrumental Variables 207
9 Simultaneous Equations Models 239
10 Basic Linear Unobserved E¤ects Panel Data Models 281
11 More Topics in Linear Unobserved Effects Models 345

III GENERAL APPROACHES TO NONLINEAR ESTIMATION 395

12 M-Estimation, Nonlinear Regression, and Quantile Regression 397
13 Maximum Likelihood Methods 469
14 Generalized Method of Moments and Minimum Distance Estimation 525

IV NONLINEAR MODELS AND RELATED TOPICS 559

15 Binary Response Models 561
16 Multinomial and Ordered Response Models 643
17 Corner Solution Responses 667
18 Count, Fractional, and Other Nonnegative Responses 723
19 Censored Data, Sample Selection, and Attrition 777
20 Stratified Sampling and Cluster Sampling 853
21 Estimating Average Treatment Effects 903
22 Duration Analysis 983

References 1025

Index

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; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage of inverse probability weighting; a more complete framework for estimating treatment effects with assumptions concerning the intervention and different data structures, including 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.