Estimation and Inference in Econometrics

Rating:
Summary: Nice Graduate Level Exposition
Review: As several other readers, I am not crazy about the geometric approach used in this book, although it is certainly very original. I love the the way the non-linear estimation is discussed. True, it is much better than Greene...

Rating:
Summary: Much Better Than Green's In Terms of Quality and Price.
Review: Green's textbook was the assigned text when I took my econometrics sequence. Like many others, I found it not well written and the explanations are pretty bad. Also, Green's is priced sky-high (around $100 for a brand new copy).

Davidson and MacKinnon is different. Both expositions and explanations are clear and easy to follow. I was so delighted after picking up a copy from the libarary. This is the one econometrics students should have. The price is also hard to beat. The reason I think it is not widely adopted is because of the geometric analysis of regression (Chapter 2). But if you don't like geometrics, you can simply skip it.

An improved version of this book is just published under the new title "Econometric Theory and Methods". This new version contains a chapter on unit-root and cointegration, as well as some new numerical methods. I urge interested buyers to take a look at the new version.

Rating:
Summary: Much Better Than Green's In Terms of Quality and Price.
Review: Green's textbook was the assigned text when I took my econometrics sequence. Like many others, I found it not well written and the explanations are pretty bad. Also, Green's is priced sky-high (around $100 for a brand new copy).

Davidson and MacKinnon is different. Both expositions and explanations are clear and easy to follow. I was so delighted after picking up a copy from the libarary. This is the one econometrics students should have. The price is also hard to beat. The reason I think it is not widely adopted is because of the geometric analysis of regression (Chapter 2). But if you don't like geometrics, you can simply skip it.

An improved version of this book is just published under the new title "Econometric Theory and Methods". This new version contains a chapter on unit-root and cointegration, as well as some new numerical methods. I urge interested buyers to take a look at the new version.

Rating:
Summary: This is the book!
Review: 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.

Rating:
Summary: Very Readable Book
Review: I recently bought a copy of this book and have read the first 5 chapters. This is not a bad book and quite easy going. I quite like the way it emphasize the M and P matrix in linear and non linear regression. But I found section 4.7 has been badly written. It tried to squeeze a lot of non elementary probalibilistic results in a few pages without giving appropriate explanations and examples. For examples, it briefly defines stationary and ergodic without further details. This book is written for economic students with only limited training in probability. I just don't know how those students will be able to understand. This is a very important section to the whole area of convergence and consistence. I hope the authors should keep this on mind for future edition.

Rating:
Summary: A good book for an intermediate econometrics course
Review: In the first year of my PhD, teachers recommended this book. I must admit that I don't appreciate too much the geometrical approach of several topics. Instead, I liked a lot the introduction given to unit root analysis and to simulation procedures. I think it's a more readable book than Greene's, and much more fun to work with. I conclude that this book is very useful for people beginning a PhD, but not for undergraduate people.

Rating:
Summary: No one like this
Review: It's a nice piece of work.
There is no one like this.
The only problem is the way the contents are presented. There is no a logical order that help us in a course. I agree that there is not a clear structured inside the chapters or in the entire work. But this is the book that reach the deepest point being readable. Another books are better structured or more intutive but too superficial or old-fashioned.
With the modern computers and software the old classical books based on small sample theory are unsuitable. Davidson and MacKinnon point us to the econometry of the future.
It would be a good idea to combine this book with Berndt's one on applied econometrics, plus a good software like Stata 8 or matrix-based programming software like MATLAB.
That's the best way to access the econometry.

Rating:
Summary: the best intermediate level textbook in econometrics
Review: This book covers the majority of standard topics in econometrics. It's very readable, you will need only knowledge of matrix algebra, calculus and basic probability in order to understand it. The book starts with geometric interpretation of least squares, and I do not understand why other reviewers complaining about that. You have to know those projection results if you want to understand what regression is about. Besides, they really simplify treatment of the subject, so it is worth to spend some time on projections. The book provides a good discussion of asymptotic theory, I think one just cannot do it better at this level of mathematics. It has a very nice presentation of GMM, nonlinear regression, maximum likelihood estimation. The discussion of Instrumental Variables estimation is just great. It also has chapters on time series econometrics, unit roots and cointegration. It's even has a small section devoted to bootstrap. In my opinion it's a must have for applied researchers in social science, and it's the best book for the first graduate course in econometrics, and it is much better than Greene (the usual choice for the first econometrics course).

Rating:
Summary: a one of the best books in econometrics
Review: This book was our recommended text for the first course in Econometrics under the Ph.D. programme in Economics. The authors took the pictorial geometric approach throughout the book. Now, geometry of this kind (pictures in two dimensions) may tell us why a result should be true, but it has serious limitations towards proving results rigorously. Even the important theorems are not well-stated, and often we have to search for the true result in a whole jungle of words. This book is very inappropriate for a graduate course; in fact, for any course, to tell the truth.

Rating:
Summary: An Excellent Book
Review: 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.