Product Description

Offering students a unifying theoretical perspective, this innovative text emphasizes nonlinear techniques of estimation, including nonlinear least squares, nonlinear instrumental variables, maximum likelihood and the generalized method of moments, but nevertheless relies heavily on simple geometrical arguments to develop intuition. One theme of the book is the use of artificial regressions for estimation, inference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, series correlation, heteroskedasticity and other types of misspecification. Other topics include the linear simultaneous equations model, non-nested hypothesis tests, influential observations and leverage, transformations of the dependent variable, binary response models, models for time-series/cross-section data, multivariate models, seasonality, unit roots and cointegration, and Monte Carlo methods, always with an emphasis on problems that arise in applied work. Explaining throughout how estimates can be obtained and tests can be carried out, the text goes beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. A comprehensive and coherent guide to the most vital topics in econometrics today, this text is indispensable for all levels of students of econometrics, economics, and statistics on regression and related topics.

Product Details

• Amazon Sales Rank: #283428 in Books
• Published on: 1993-01-14
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 896 pages

Review
"An important reference source for both the theoretical and applied researcher....More importantly, the authors' view of the areas presented is cohesive, and they provide an open-ended discussion, so that the book can serve as a source of research topics as well as a reference. From this standpoint, it is very good reading for a doctoral student....Davidson and MacKinnon's book is sure to have an impact on the way econometrics is taught; my hope is that the geometric approach, widely and quite consistently used by the authors, will be adopted in the exposition of regression, illustration of the classical test statistics, and examination of test power. Certainly, the tool of projection orthogonally to part of the regression space (the Frisch-Waugh-Lovell theorem) should be adopted more widely for its convenience in simplifying many derivations."--Econometric Theory

"Well-written advanced textbook in econometrics, suitable for seminar courses. With its lucid analysis, it emerges as an extremely useful tool for applied econometricians."--Madhu Mohanty, California State University

"Clearly written and makes clear a lot of links between different estimation procedures."--Curtis J. Simon, Clemson University

"Good coverage of standard econometric theory."--M.M. Ali, University of Kentucky

"Coverage of the geometry of least squares is excellent."--Doug Steigerwald, University of California, Santa Barbara

"This is a unique and fascinating book. It's the only econometrics textbook that has ever given me the urge to read it from cover to cover."--Stratford Douglas, West Virginia University

"A wonderful text. The book is comprehensive and has a most authoritative discussion of topics of current interest such as cointegration, nonlinear simultaneous equation models, specification testing, etc."--Sunil Sapra, California State University at Los Angeles

"Great book! Good reference for anyone wishing to get an overview of the state of the art. Good pace, topic selection, level of difficulty. Also, good use of notation."--Dean Allen Schiffman, University of California, San Diego

"This is the most up-to-date econometrics textbook. It deals with topics which were so far discussed only in journal articles....A must book for any higher level graduate econometrics course."--Professor Anil K. Bera, University of Illinois

"Extremely valuable in the sense that it balances the coverage between test of hypothesis and estimation. Most books treat test of hypothesis as a side issue. The book is well-contained and easy to read. An excellent textbook."--Choon-Geol Moon, Rutgers University

About the Author
Russell Davidson and James G. MacKinnon are both at Queen's University.

Customer Reviews

An Excellent Book
This is one of the best books on econometrics published in the past few years. The authors use the theory of vector spaces (projection operators in a Euclidian space) to show how the intuition behind the General Linear Model extends in a natural way to more complex nonlinear models. The authors demonstrate that sophisticated maximum likelihood (or simulated maximum likelihood) estimation algorithms are essentially repeated applications of the linear projection operators seen in regression context. The result is a unified theory of econometrics which takes readers from a "cookbook" level of statistical sophistication to a more mature "model building" orientation.

In short, this is one of the most refreshing treatments of econometrics I've seen in many years. University instructors -- particulary those teaching doctoral level courses -- should seriously consider adopting this as a text.

This is the book!
I do not know better book on nonlinear estimation and inference in econometrics.

Overall the book is very well written and relatively easy to understand, considering its subject. However, if you have not been introduced to linear econometrics, the book can become very hard, mainly if the reader is not acquainted with matrix algebra.

The first chapter on the geometrics of regression is simply marvelous, although a better picture is in Ruud's.

The style is someway formal, but different from the traditional lemma-theorem-proof-corollary way. This makes the book easier to read.

Future improvements include:

a. More examples (please);
b. Make the early 2 chapters on asymptotics clearer;
c. Extend the GMM approach interconnecting it with other chapters (it's more general);
d. Put exercises, with solutions, with selected solutions, whatever, but exercises, including computational ones;
e. Some economics - this does not mean applications per se, but it means to explain where and why such techniques are necessary in the real world.

Comparison to Hayashi
We were recommended to use this book as a complement to Hayashi, which we had used as our initial primary text for the 2nd and 3rd quarter of a first-year graduate econometrics sequence.

I think I would have found the exposition here rather challenging had this been my initial text. A few comparisons between the two books:

H - GMM as organizing principle.
D&M - Least squares as organizing principle.
I think the latter was in many ways a more intuitive way of viewing these techniques (for me), but perhaps provides a less fully integrated view of the estimators.

H - Matrix algebra and first order conditions as justifying estimation techniques.
D&M - Geometric projection as justifying estimation techniques.
The geometry is a powerful tool for understanding these concepts, but I think serves me better as a complement rather than a primary motivator.

H - Treats homoskedasticity and lack of serial correlation as special cases.
D&M - Treats heteroskedasticity and serial correlation as extensions of iid models.

H - Treats nonlinear models as extensions.
D&M - Treats linear models as special cases.

H - Offers a large number of economic applications.
D&M - Basically entirely theoretical in its justification of theorems and techniques.
This would be among the most frustrating things about using D&M as a primary text.

Just a few thoughts that might be useful to someone considering this book. The organization around least squares is very useful, I think, and a geometric intuition for econometrics must be a powerful tool as one progresses in the field.