Econometric Analysis of Cross Section and Panel Data, Second Edition

0002/E2

Wooldridge, Jeffrey M. 저자(글)

MIT Press · 2010년 10월 01일

책 소개

This second edition provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods.
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 highly recommend this book for graduate classes in econometrics. We have used it at MIT and the students find it extremely helpful. Wooldridge covers topics in a highly readable and insightful way." - Jerry Hausman, John and Jennie S. MacDonald Professor of Economics, MIT
"I highly recommend this book for graduate classes in econometrics. We have used it at MIT and the students find it extremely helpful. Wooldridge covers topics in a highly readable and insightful way." -Jerry Hausman, John and Jennie S. MacDonald Professor of Economics, MIT
"In this leading econometrics textbook, Wooldridge offers a very good explanation of the basics of the field making it a great resource for econometrics students and a contemporary treatment of many important topics, making it a wonderful reference for researchers as well. The new edition provides clear explanations of many recent developments." - Whitney Newey, Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics, MIT
"In this leading econometrics textbook, Wooldridge offers a very good explanation of the basics of the field making it a great resource for econometrics students and a contemporary treatment of many important topics, making it a wonderful reference for researchers as well. The new edition provides clear explanations of many recent developments." -Whitney Newey, Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics, MIT
"This second edition provides a comprehensive, accessible, and updated treatment of cross section and panel data methods. The book is full of useful insights, applications, and worked problems. It will serve as an invaluable textbook and reference for graduate students and researchers alike." - Richard Blundell, Institute for Fiscal Studies, University College London
"This second edition provides a comprehensive, accessible, and updated treatment of cross section and panel data methods. The book is full of useful insights, applications, and worked problems. It will serve as an invaluable textbook and reference for graduate students and researchers alike." -Richard Blundell, Institute for Fiscal Studies, University College London
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.
"I highly recommend this book for graduate classes in econometrics. We have used it at MIT and the students find it extremely helpful. Wooldridge covers topics in a highly readable and insightful way." Jerry Hausman , John and Jennie S. MacDonald Professor of Economics, MIT
"In this leading econometrics textbook, Wooldridge offers a very good explanation of the basics of the field -- making it a great resource for econometrics students -- and a contemporary treatment of many important topics, making it a wonderful reference for researchers as well. The new edition provides clear explanations of many recent developments." Whitney Newey , Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics, MIT
"This second edition provides a comprehensive, accessible, and updated treatment of cross section and panel data methods. The book is full of useful insights, applications, and worked problems. It will serve as an invaluable textbook and reference for graduate students and researchers alike." Richard Blundell , Institute for Fiscal Studies, University College London

작가정보

저자(글) Wooldridge, Jeffrey M.

목차

• Preface	p. xxi
  Acknowledgements	p. xxix
  Introduction and Background
  Introduction
  Causal Relationships and Ceteris Paribus Analysis	p. 3
  Stochastic Setting and Asymptotic Analysis	p. 4
  Some Examples	p. 7
  Why Not Fixed Explanatory Variables?	p. 9
  Conditional Expectations and Related Concepts in Econometrics
  Role of Conditional Expectations in Econometrics	p. 13
  Features of Conditional Expectations	p. 14
  Linear Projections	p. 25
  Problems	p. 27
  Appendix 2A	p. 30
  Basic Asymptotic Theory
  Convergence of Deterministic Sequences	p. 37
  Convergence in Probability and Boundedness in Probability	p. 38
  Convergence in Distribution	p. 40
  Limit Theorems for Random Samples	p. 41
  Limiting Behavior of Estimators and Test Statistics	p. 42
  Problems	p. 47
  Linear Models
  Single-Equation Linear Model and Ordinary Least Squares Estimation
  Overview of the Single-Equation Linear Model	p. 53
  Asymptotic Properties of Ordinary Least Squares	p. 55
  Ordinary Least Squares Solutions to the Omitted Variables Problem	p. 65
  Properties of Ordinary Least Squares under Measurement Error	p. 76
  Instrumental Variables Estimation of Single-Equation Linear Models
  Instrumental Variables and Two-Stage Least Squares	p. 89
  General Treatment of Two-Stage Least Squares	p. 98
  IV Solutions to the Omitted Variables and Measurement Error Problems	p. 112
  Problems	p. 115
  Additional Single-Equation Topics
  Estimation with Generated Regressors and Instruments	p. 123
  Control Function Approach to Endogeneity	p. 126
  Some Specification Tests	p. 129
  Correlated Random Coe‰cient Models	p. 141
  Pooled Cross Sections and Di?rence-in-Di?rences Estimation	p. 146
  Problems	p. 152
  Appendix 6A	p. 157
  Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares
  Introduction	p. 161
  Some Examples	p. 161
  System Ordinary Least Squares Estimation of a Multivariate Linear System	p. 166
  Consistency and Asymptotic Normality of Generalized Least Squares	p. 173
  Feasible Generalized Least Squares	p. 176
  Testing the Use of Feasible Generalized Least Squares	p. 183
  Seemingly Unrelated Regressions, Revisited	p. 185
  Linear Panel Data Model, Revisited	p. 191
  Problems	p. 202
  System Estimation by Instrumental Variables
  Introduction and Examples	p. 207
  General Linear System of Equations	p. 210
  Generalized Method of Moments Estimation	p. 213
  Generalized Instrumental Variables Estimator	p. 222
  Testing Using Generalized Method of Moments	p. 226
  More E‰cient Estimation and Optimal Instruments	p. 229
  Summary Comments on Choosing an Estimator	p. 232
  Problems	p. 233
  Simultaneous Equations Models
  Scope of Simultaneous Equations Models	p. 239
  Identification in a Linear System	p. 241
  Estimation after Identification	p. 252
  Additional Topics in Linear Simultaneous Equations Methods	p. 256
  Simultaneous Equations Models Nonlinear in Endogenous Variables	p. 262
  Di?rent Instruments for Di?rent Equations	p. 271
  Problems	p. 273
  Basic Linear Unobserved E?cts Panel Data Models
  Motivation: Omitted Variables Problem	p. 281
  Assumptions about the Unobserved E?cts and Explanatory Variables	p. 285
  Estimating Unobserved E?cts Models by Pooled Ordinary Least Squares	p. 291
  Random E?cts Methods	p. 292
  Fixed E?cts Methods	p. 300
  First Differencing Methods	p. 315
  Comparison of Estimators	p. 321
  Problems	p. 334
  More Topics in Linear Unobserved Effects Models
  Generalized Method of Moments Approaches to the Standard Linear Unobserved E?cts Model	p. 345
  Random and Fixed E?cts Instrumental Variables Methods	p. 349
  Hausman and Taylor-Type Models	p. 358
  First Di?rencing Instrumental Variables Methods	p. 361
  Unobserved Effects Models with Measurement Error	p. 365
  Estimation under Sequential Exogeneity	p. 368
  Models with Individual-Specific Slopes	p. 374
  Problems	p. 387
  General Approaches to Nonlinear Estimation
  M-Estimation, Nonlinear Regression, and Quantile Regression
  Introduction	p. 397
  Identification, Uniform Convergence, and Consistency	p. 401
  Asymptotic Normality	p. 405
  Two-Step M-Estimators	p. 409
  Estimating the Asymptotic Variance	p. 413
  Hypothesis Testing	p. 420
  Optimization Methods	p. 431
  Simulation and Resampling Methods	p. 436
  Multivariate Nonlinear Regression Methods	p. 442
  Quantile Estimation	p. 449
  Problems	p. 462
  Maximum Likelihood Methods
  Introduction	p. 469
  Preliminaries and Examples	p. 470
  General Framework for Conditional Maximum Likelihood Estimation	p. 473
  Consistency of Conditional Maximum Likelihood Estimation	p. 475
  Asymptotic Normality and Asymptotic Variance Estimation	p. 476
  Hypothesis Testing	p. 481
  Specification Testing	p. 482
  Partial (or Pooled) Likelihood Methods for Panel Data	p. 485
  Panel Data Models with Unobserved E?cts	p. 494
  Two-Step Estimators Involving Maximum Likelihood	p. 499
  Quasi-Maximum Likelihood Estimation	p. 502
  Problems	p. 517
  Appendix 13A	p. 522
  Generalized Method of Moments and Minimum Distance Estimation
  Asymptotic Properties of Generalized Method of Moments	p. 525
  Estimation under Orthogonality Conditions	p. 530
  Systems of Nonlinear Equations	p. 532
  Efficient Estimation	p. 538
  Classical Minimum Distance Estimation	p. 545
  Panel Data Applications	p. 547
  Problems	p. 555
  Appendix 14A	p. 558
  Nonlinear Models and Related Topics
  Binary Response Models
  Introduction	p. 561
  Linear Probability Model for Binary Response	p. 562
  Index Models for Binary Response: Probit and Logit	p. 565
  Maximum Likelihood Estimation of Binary Response Index Models	p. 567
  Testing in Binary Response Index Models	p. 569
  Reporting the Results for Probit and Logit	p. 573
  Specification Issues in Binary Response Models	p. 582
  Binary Response Models for Panel Data	p. 608
  Problems	p. 635
  Multinomial and Ordered Response Models
  Introduction	p. 643
  Multinomial Response Models	p. 643
  Ordered Response Models	p. 655
  Problems	p. 663
  Corner Solution Responses
  Motivation and Examples	p. 667
  Useful Expressions for Type I Tobit	p. 671
  Estimation and Inference with the Type I Tobit Model	p. 676
  Reporting the Results	p. 677
  Specification Issues in Tobit Models	p. 680
  Two-Part Models and Type II Tobit for Corner Solutions	p. 690
  Two-Limit Tobit Model	p. 703
  Panel Data Methods	p. 705
  Problems	p. 715
  Count, Fractional, and Other Nonnegative Responses
  Introduction	p. 723
  Poisson Regression	p. 724
  Other Count Data Regression Models	p. 736
  Gamma (Exponential) Regression Model	p. 740
  Endogeneity with an Exponential Regression Function	p. 742
  Fractional Responses	p. 748
  Panel Data Methods	p. 755
  Problems	p. 769
  Censored Data, Sample Selection, and Attrition
  Introduction	p. 777
  Data Censoring	p. 778
  Overview of Sample Selection	p. 790
  When Can Sample Selection Be Ignored?	p. 792
  Selection on the Basis of the Response Variable: Truncated Regression	p. 799
  Incidental Truncation: A Probit Selection Equation	p. 802
  Incidental Truncation: A Tobit Selection Equation	p. 815
  Inverse Probability Weighting for Missing Data	p. 821
  Sample Selection and Attrition in Linear Panel Data Models	p. 827
  Problems	p. 845
  Stratified Sampling and Cluster Sampling
  Introduction	p. 854
  Stratified Sampling	p. 854
  Cluster Sampling	p. 863
  Complex Survey Sampling	p. 894
  Problems	p. 899
  Estimating Average Treatment Effects
  Introduction	p. 903
  A Counterfactual Setting and the Self-Selection Problem	p. 904
  Methods Assuming Ignorability (or Unconfoundedness) of Treatment	p. 908
  Instrumental Variables Methods	p. 937
  Regression Discontinuity Designs	p. 954
  Further Issues	p. 960
  Problems	p. 975
  Duration Analysis
  Introduction	p. 983
  Hazard Functions	p. 984
  Analysis of Single-Spell Data with Time-Invariant Covariates	p. 991
  Analysis of Grouped Duration Data	p. 1010
  Further Issues	p. 1018
  Problems	p. 1019
  References	p. 1025
  Index	p. 1045
  Table of Contents provided by Publisher. All Rights Reserved.

기본정보

ISBN	9780262232586 ( 0262232588 )
발행(출시)일자	2010년 10월 01일
쪽수	1096쪽
크기	209 * 235 * 46 mm / 1868 g
총권수	1권
언어	영어