Summary
For a oneyear graduate course in Econometrics. This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficient theoretical background that they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. The Fifth Edition features a complete update of techniques and developments, a reorganization of material for improved presentation, and new material and applications.
Table of Contents
Preface 

xxvii  


1  (6) 

The Classical Multiple Linear Regression Model 


7  (12) 


19  (22) 

FiniteSample Properties of the Least Squares Estimator 


41  (24) 

LargeSample Properties of the Least Squares and Instrumental Variables Estimators 


65  (28) 


93  (23) 

Functional Form and Structural Change 


116  (32) 

Specification Analysis and Model Selection 


148  (14) 

Nonlinear Regression Models 


162  (29) 

Nonspherical DisturbancesThe Generalized Regression Model 


191  (24) 


215  (35) 


250  (33) 


283  (56) 

Systems of Regression Equations 


339  (39) 

SimultaneousEquations Models 


378  (47) 

Estimation Frameworks in Econometrics 


425  (43) 

Maximum Likelihood Estimation 


468  (57) 

The Generalized Method of Moments 


525  (33) 

Models with Lagged Variables 


558  (50) 


608  (55) 

Models for Discrete Choice 


663  (93) 

Limited Dependent Variable and Duration Models 


756  (47) 
Appendix A Matrix Algebra 

803  (42) 
Appendix B Probability and Distribution Theory 

845  (32) 
Appendix C Estimation and Inference 

877  (19) 
Appendix D Large Sample Distribution Theory 

896  (23) 
Appendix E Computation and Optimization 

919  (27) 
Appendix F Data Sets Used in Applications 

946  (7) 
Appendix G Statistical Tables 

953  (6) 
References 

959  (36) 
Author Index 

995  (7) 
Subject Index 

1002  
Excerpts
THE FIFTH EDITION OF ECONOMETRIC ANALYSIS Econometric Analysisis intended for a oneyear graduate course in econometrics for social scientists. The prerequisites for this course should include calculus, mathematical statistics, and an introduction to econometrics at the level of, say,Gujarati's Basic Econometrics(McGrawHill, 1995) or Wooldridge'sIntroductory Econometrics: A Modern ApproachSouthWestern (2000). Selfcontained (for our purposes) summaries of the matrix algebra, mathematical statistics, and statistical theory used later in the book are given in Appendices A through D. Appendix E contains a description of numerical methods that will be useful to practicing econometricians. The formal presentation of econometrics begins with discussion of a fundamental pillar, the linear multiple regression model, in Chapters 2 through 8. Chapters 9 through 15 present familiar extensions of the single linear equation model, including nonlinear regression, panel data models, the generalized regression model, and systems of equations. The linear model is usually not the sole technique used in most of the contemporary literature. In view of this, the (expanding) second half of this book is devoted to topics that will extend the linear regression model in many directions. Chapters 16 through 18 present the techniques and underlying theory of estimation in econometrics, including GMM and maximum likelihood estimation methods and simulation based techniques. We end in the last four chapters, 19 through 22, with discussions of current topics in applied econometrics, including timeseries analysis and the analysis of discrete choice and limited dependent variable models. This book has two objectives. The first is to introduce students toapplied econometrics,including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficienttheoretical backgroundthat they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. Thus, I have spent what might seem to be a large amount of effort explaining the mechanics of GMM estimation, nonlinear least squares, and maximum likelihood estimation and GARCH models. To meet the second objective, this book also contains a fair amount of theoretical material, such as that on maximum likelihood estimation and on asymptotic results for regression models. Modern software has made complicated modeling very easy to do, and an understanding of the underlying theory is important. I had several purposes in undertaking this revision. As in the past, readers continue to send me interesting ideas for my "next edition." It is impossible to use them all, of course. Because the five volumes of theHandbook of Econometricsand two of theHandbook of Applied Econometricsalready run to over 4,000 pages, it is also unnecessary. Nonetheless, this revision is appropriate for several reasons. First, there are new and interesting developments in the field, particularly in the areas of microeconometrics (panel data, models for discrete choice) and, of course, in time series, which continues its rapid development. Second, I have taken the opportunity to continue finetuning the text as the experience and shared wisdom of my readers accumulates in my files. For this revision, that adjustment has entailed a substantial rearrangement of the materialthe main purpose of that was to allow me to add the new material in a more compact and orderly way than I could have with the table of contents in the 4th edition. The 15terature in econometrics has continued to evolve, and my third objective is to grow with it. This purpose is inherently difficult to accomplish in a textbook. Most of the literature is written by professionals for other professionals,