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Econometric Analysis of Cross Section and Panel Data 2ed

by Jeffrey M Wooldridge The MIT Press
Pub Date:
10/2010
ISBN:
9780262232586
Format:
Hbk 1096 pages
Price:
AU$233.00 NZ$243.48
Product Status: In Stock Now
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Instructors
& Academics:
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.

CONTENTS: Preface / Acknowledgements / I INTRODUCTION AND BACKGROUND / Conditional Expectations and Related Concepts in Econometrics / Basic Asymptotic Theory / II LINEAR MODELS / Single-Equation Linear Model and Ordinary Least Squares Estimation / Instrumental Variables Estimation of Single-Equation Linear Models / Additional Single-Equation Topics / Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares / System Estimation by Instrumental Variables / Simultaneous Equations Models / Basic Linear Unobserved E¤ects Panel Data Models / More Topics in Linear Unobserved Effects Models / III GENERAL APPROACHES TO NONLINEAR ESTIMATION / M-Estimation, Nonlinear Regression, and Quantile Regression / Maximum Likelihood Methods / Generalized Method of Moments and Minimum Distance Estimation / IV NONLINEAR MODELS AND RELATED TOPICS / Binary Response Models / Multinomial and Ordered Response Models / Corner Solution Responses / Count, Fractional, and Other Nonnegative Responses / Censored Data, Sample Selection, and Attrition / Stratified Sampling and Cluster Sampling / Estimating Average Treatment Effects / Duration Analysis / References / Index.

Complete Table of Contents

Preface xxi

Acknowledgements xxix

I INTRODUCTION AND BACKGROUND 1

1 Introduction 3

1.1 Causal Relationships and Ceteris Paribus Analysis 3

1.2 Stochastic Setting and Asymptotic Analysis 4

1.3 Some Examples 7

1.4 Why Not Fixed Explanatory Variables? 9

2 Conditional Expectations and Related Concepts in Econometrics 13

2.1 Role of Conditional Expectations in Econometrics 13

2.2 Features of Conditional Expectations 14

2.3 Linear Projections 25

Problems 27

Appendix 2A 30

3 Basic Asymptotic Theory 37

3.1 Convergence of Deterministic Sequences 37

3.2 Convergence in Probability and Boundedness in Probability 38

3.3 Convergence in Distribution 40

3.4 Limit Theorems for Random Samples 41

3.5 Limiting Behavior of Estimators and Test Statistics 42

Problems 47

II LINEAR MODELS 51

4 Single-Equation Linear Model and Ordinary Least Squares Estimation 53

4.1 Overview of the Single-Equation Linear Model 53

4.2 Asymptotic Properties of Ordinary Least Squares 55

4.3 Ordinary Least Squares Solutions to the Omitted Variables Problem 65

4.4 Properties of Ordinary Least Squares under Measurement Error 76

5 Instrumental Variables Estimation of Single-Equation Linear Models 89

5.1 Instrumental Variables and Two-Stage Least Squares 89

5.2 General Treatment of Two-Stage Least Squares 98

5.3 IV Solutions to the Omitted Variables and Measurement Error Problems 112

Problems 115

6 Additional Single-Equation Topics 123

6.1 Estimation with Generated Regressors and Instruments 123

6.2 Control Function Approach to Endogeneity 126

6.3 Some Specification Tests 129

6.4 Correlated Random Coe‰cient Models 141

6.5 Pooled Cross Sections and Di¤erence-in-Di¤erences Estimation 146

Problems 152

Appendix 6A 157

7 Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares 161

7.1 Introduction 161

7.2 Some Examples 161

7.3 System Ordinary Least Squares Estimation of a Multivariate Linear System 166

7.4 Consistency and Asymptotic Normality of Generalized Least Squares 173

7.5 Feasible Generalized Least Squares 176

7.6 Testing the Use of Feasible Generalized Least Squares 183

7.7 Seemingly Unrelated Regressions, Revisited 185

7.8 Linear Panel Data Model, Revisited 191

Problems 202

8 System Estimation by Instrumental Variables 207

8.1 Introduction and Examples 207

8.2 General Linear System of Equations 210

8.3 Generalized Method of Moments Estimation 213

8.4 Generalized Instrumental Variables Estimator 222

8.5 Testing Using Generalized Method of Moments 226

8.6 More E‰cient Estimation and Optimal Instruments 229

8.7 Summary Comments on Choosing an Estimator 232

Problems 233

9 Simultaneous Equations Models 239

9.1 Scope of Simultaneous Equations Models 239

9.2 Identification in a Linear System 241

9.3 Estimation after Identification 252

9.4 Additional Topics in Linear Simultaneous Equations Methods 256

9.5 Simultaneous Equations Models Nonlinear in Endogenous Variables 262

9.6 Di¤erent Instruments for Di¤erent Equations 271

Problems 273

10 Basic Linear Unobserved E¤ects Panel Data Models 281

10.1 Motivation: Omitted Variables Problem 281

10.2 Assumptions about the Unobserved E¤ects and Explanatory Variables 285

10.3 Estimating Unobserved E¤ects Models by Pooled Ordinary Least Squares 291

10.4 Random E¤ects Methods 292

10.5 Fixed E¤ects Methods 300

10.6 First Differencing Methods 315

10.7 Comparison of Estimators 321

Problems 334

11 More Topics in Linear Unobserved Effects Models 345

11.1 Generalized Method of Moments Approaches to the Standard Linear Unobserved E¤ects Model 345

11.2 Random and Fixed E¤ects Instrumental Variables Methods 349

11.3 Hausman and Taylor–Type Models 358

11.4 First Di¤erencing Instrumental Variables Methods 361

11.5 Unobserved Effects Models with Measurement Error 365

11.6 Estimation under Sequential Exogeneity 368

11.7 Models with Individual-Specific Slopes 374

Problems 387

III GENERAL APPROACHES TO NONLINEAR ESTIMATION 395

12 M-Estimation, Nonlinear Regression, and Quantile Regression 397

12.1 Introduction 397

12.2 Identification, Uniform Convergence, and Consistency 401

12.3 Asymptotic Normality 405

12.4 Two-Step M-Estimators 409

12.5 Estimating the Asymptotic Variance 413

12.6 Hypothesis Testing 420

12.7 Optimization Methods 431

12.8 Simulation and Resampling Methods 436

12.9 Multivariate Nonlinear Regression Methods 442

12.10 Quantile Estimation 449

Problems 462

13 Maximum Likelihood Methods 469

13.1 Introduction 469

13.2 Preliminaries and Examples 470

13.3 General Framework for Conditional Maximum Likelihood Estimation 473

13.4 Consistency of Conditional Maximum Likelihood Estimation 475

13.5 Asymptotic Normality and Asymptotic Variance Estimation 476

13.6 Hypothesis Testing 481

13.7 Specification Testing 482

13.8 Partial (or Pooled) Likelihood Methods for Panel Data 485

13.9 Panel Data Models with Unobserved E¤ects 494

13.10 Two-Step Estimators Involving Maximum Likelihood 499

13.11 Quasi-Maximum Likelihood Estimation 502

Problems 517

Appendix 13A 522

14 Generalized Method of Moments and Minimum Distance Estimation 525

14.1 Asymptotic Properties of Generalized Method of Moments 525

14.2 Estimation under Orthogonality Conditions 530

14.3 Systems of Nonlinear Equations 532

14.4 Efficient Estimation 538

14.5 Classical Minimum Distance Estimation 545

14.6 Panel Data Applications 547

Problems 555

Appendix 14A 558

IV NONLINEAR MODELS AND RELATED TOPICS 559

15 Binary Response Models 561

15.1 Introduction 561

15.2 Linear Probability Model for Binary Response 562

15.3 Index Models for Binary Response: Probit and Logit 565

15.4 Maximum Likelihood Estimation of Binary Response Index Models 567

15.5 Testing in Binary Response Index Models 569

15.6 Reporting the Results for Probit and Logit 573

15.7 Specification Issues in Binary Response Models 582

15.8 Binary Response Models for Panel Data 608

Problems 635

16 Multinomial and Ordered Response Models 643

16.1 Introduction 643

16.2 Multinomial Response Models 643

16.3 Ordered Response Models 655

Problems 663

17 Corner Solution Responses 667

17.1 Motivation and Examples 667

17.2 Useful Expressions for Type I Tobit 671

17.3 Estimation and Inference with the Type I Tobit Model 676

17.4 Reporting the Results 677

17.5 Specification Issues in Tobit Models 680

17.6 Two-Part Models and Type II Tobit for Corner Solutions 690

17.7 Two-Limit Tobit Model 703

17.8 Panel Data Methods 705

Problems 715

18 Count, Fractional, and Other Nonnegative Responses 723

18.1 Introduction 723

18.2 Poisson Regression 724

18.3 Other Count Data Regression Models 736

18.4 Gamma (Exponential) Regression Model 740

18.5 Endogeneity with an Exponential Regression Function 742

18.6 Fractional Responses 748

18.7 Panel Data Methods 755

Problems 769

19 Censored Data, Sample Selection, and Attrition 777

19.1 Introduction 777

19.2 Data Censoring 778

19.3 Overview of Sample Selection 790

19.4 When Can Sample Selection Be Ignored? 792

19.5 Selection on the Basis of the Response Variable: Truncated Regression 799

19.6 Incidental Truncation: A Probit Selection Equation 802

19.7 Incidental Truncation: A Tobit Selection Equation 815

19.8 Inverse Probability Weighting for Missing Data 821

19.9 Sample Selection and Attrition in Linear Panel Data Models 827

Problems 845

20 Stratified Sampling and Cluster Sampling 853

20.1 Introduction 854

20.2 Stratified Sampling 854

20.3 Cluster Sampling 863

20.4 Complex Survey Sampling 894

Problems 899

21 Estimating Average Treatment Effects 903

21.1 Introduction 903

21.2 A Counterfactual Setting and the Self-Selection Problem 904

21.36 Methods Assuming Ignorability (or Unconfoundedness) of Treatment 908

21.4 Instrumental Variables Methods 937

21.5 Regression Discontinuity Designs 954

21.6 Further Issues 960

Problems 975

22 Duration Analysis 983

22.1 Introduction 983

22.2 Hazard Functions 984

22.3 Analysis of Single-Spell Data with Time-Invariant Covariates 991

22.4 Analysis of Grouped Duration Data 1010

22.5 Further Issues 1018

Problems 1019


--------------------------------------------------------------------------------

References 1025

Index

Jeffrey M. Wooldridge is University Distinguished Professor of Economics at Michigan State University and a Fellow of the Econometric Society.