Estimation and Inference in Econometrics [Hardcover]

Russell Davidson (Author), James G. MacKinnon (Author)

Book Description

Publication Date: January 14, 1993 | ISBN-10: 0195060113 | ISBN-13: 978-0195060119

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Editorial Reviews

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.

Product Details

• Hardcover: 896 pages
• Publisher: Oxford University Press, USA (January 14, 1993)
• Language: English
• ISBN-10: 0195060113
• ISBN-13: 978-0195060119
• Product Dimensions: 9.3 x 6.4 x 1.9 inches
• Shipping Weight: 3 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (14 customer reviews)
• Amazon Best Sellers Rank: #803,748 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

13 of 13 people found the following review helpful:

5.0 out of 5 stars An Excellent Book, September 12, 1998

By A Customer

This review is from: Estimation and Inference in Econometrics (Hardcover)

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.

12 of 12 people found the following review helpful:

3.0 out of 5 stars Comparison to Hayashi, March 4, 2007

By 

Luke (California, USA) - See all my reviews

Amazon Verified Purchase

This review is from: Estimation and Inference in Econometrics (Hardcover)

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.

8 of 8 people found the following review helpful:

4.0 out of 5 stars This is the book!, March 27, 2002

By 

Rodrigo D. S. Bueno (Sao Paulo - Brazil) - See all my reviews
(REAL NAME)   

This review is from: Estimation and Inference in Econometrics (Hardcover)

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.