Econometric Analysis (5th Edition)

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9780130661890: Econometric Analysis (5th Edition)

For a one-year 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.

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From the Back Cover:

Key Features of ECONOMETRIC ANALYSIS, Fifth Edition:
  • Major reorganization of material. The entire text has been reconstructed to help students' understanding of the material.
  • Applied orientation. Students learn how to do econometrics.
  • Balanced coverage of topics. Includes Classical, Bayesian, GMM, maximum likelihood, cross section, and more time series and panel data.
  • Applications and numerical examples. Detailed solutions to applications that show students how to do the computations.
  • Attention to both linear and nonlinear techniques. Software has made nonlinear estimation easy, inclusion of this information is now available.
  • Students who purchase the text can visit www.prenhall.com/greene to download the most current LIMDEP software and data sets.

Excerpt. Reprinted by permission. All rights reserved.:

THE FIFTH EDITION OF ECONOMETRIC ANALYSIS

Econometric Analysis is intended for a one-year 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 (McGraw-Hill, 1995) or Wooldridge's Introductory Econometrics: A Modern Approach South-Western (2000). Self-contained (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 time-series analysis and the analysis of discrete choice and limited dependent variable models.

This book 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. 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 the Handbook of Econometrics and two of the Handbook of Applied Econometrics already 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 fine-tuning 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 material—the 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, and this textbook is written for students who are in the early stages of their training. But I do hope to provide a bridge to that literature, both theoretical and applied.

This book is a broad survey of the field of econometrics. This field grows continually, and such an effort becomes increasingly difficult. (A partial list of journals devoted at least in part, if not completely, to econometrics now includes the Journal of Applied Econometrics, Journal of Econometrics, Econometric Theory, Econometric Reviews, Journal of Business and Economic Statistics, Empirical Economics, and Econometrica.) Still, my view has always been that the serious student of the field must start somewhere, and one can successfully seek that objective in a single textbook. This text attempts to survey, at an entry level, enough of the fields in econometrics that a student can comfortably move from here to practice or more advanced study in one or more specialized areas. At the same time, I have tried to present the material in sufficient generality that the reader is also able to appreciate the important common foundation of all these fields and to use the tools that they all employ.

There are now quite a few recently published texts in econometrics. Several have gathered in compact, elegant treatises, the increasingly advanced and advancing theoretical background of econometrics. Others, such as this book, focus more attention on applications of econometrics. One feature that distinguishes this work from its predecessors is its greater emphasis on nonlinear models. Davidson and MacKinnon (1993) is a noteworthy, but more advanced, exception. Computer software now in wide use has made estimation of nonlinear models as routine as estimation of linear ones, and the recent literature reflects that progression. My purpose is to provide a textbook treatment that is in fine with current practice. The book concludes with four lengthy chapters on time-series analysis, discrete choice models and limited dependent variable models. These nonlinear models are now the staples of the applied econometrics literature. This book also contains a fair amount of material that will extend beyond many first courses in econometrics, including, perhaps, the aforementioned chapters on limited dependent variables, the section in Chapter 22 on duration models, and some of the discussions of time series and panel data models. Once again, I have included these in the hope of providing a bridge to the professional literature in these areas.

I have had one overriding purpose that has motivated all five editions of this work. For the vast majority of readers of books such as this, whose ambition is to use, not develop econometrics, I believe that it is simply not sufficient to recite the theory of estimation, hypothesis testing and econometric analysis. Understanding the often subtle background theory is extremely important. But, at the end of the day, my purpose in writing this work, and for my continuing efforts to update it in this now fifth edition, is to show readers how to do econometric analysis. I unabashedly accept the unflattering assessment of a correspondent who once likened this book to a "user's guide to econometrics."

SOFTWARE AND DATA

There are many computer programs that are widely used for the computations described in this book. All were written by econometricians or statisticians, and in general, all are regularly updated to incorporate new developments in applied econometrics. A sampling of the most widely used packages and Internet home pages where you can find information about them are:

  • E-Views—www.eviews.com (QMS, Irvine, Calif.)
  • Gauss—www.aptech.com (Aptech Systems, Kent, Wash.)
  • LIMDEP—www.limdep.com (Econometric Software, Plainview, N.Y)
  • RATS—www.estima.com (Estima, Evanston, Ill.)
  • SAS—www.sas.com (SAS, Cary, N.C.)
  • Shazam—shazam.econ.ubc.ca (Ken White, UBC, Vancouver, B.C.)
  • Stata—www.stata.com (Stata, College Station, Tex.)
  • TSP—www.tspintl.com (TSP International, Stanford, Calif)

Programs vary in size, complexity, cost, the amount of programming required of the user, and so on. Journals such as The American Statistician, The Journal of Applied Econometrics, and The Journal of Economic Surveys regularly publish reviews of individual packages and comparative surveys of packages, usually with reference to particular functionality such as panel data analysis or forecasting.

With only a few exceptions, the computations described in this book can be carried out with any of these packages. We hesitate to link this text to any of them in particular. We have placed for general access a customized version of LIMDEP, which was also written by the author, on the website for this text, www.prenhall.com/greene. LIMDEP programs used for many of the computations are posted on the sites as well.

The data sets used in the examples are also on the website. Throughout the text, these data sets are referred to "TableFn.m," for example Table F4.1. The F refers to Appendix F at the back of the text, which contains descriptions of the data sets. The actual data are posted on the website with the other supplementary materials for the text. (The data sets are also replicated in the system format of most of the commonly used econometrics computer programs, including in addition to LIMDEP, SAS, TSP, SPSS, E-Views,Stata, so that you can easily import them into whatever program you might be using.)

I should also note, there are now thousands of interesting websites containing software, data sets, papers, and commentary on econometrics. It would be hopeless to attempt any kind of a survey here. But, I do note one which is particularly agreeably structured and well targeted for readers of this book, the data archive for the Journal of Applied Econometrics. This journ...

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