 |
GetTextbooks.co.uk
Compare Prices & Save up to 90% |
|
|
|
| Login | Sign up | My Wish List |
 | | Estimation and Inference in Econometrics by Russell Davidson, James G. MacKinnon ISBN-10: 9780195060119 ISBN-10: 0-19-506011-3 ISBN-13: 9780195060119 ISBN-13: 978-0-19-506011-9 Hardcover 1993-01-14 Oxford University Press, USA Find Lowest Price |
Editorials | ||
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. | ||
Reviews | ||
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. | ||
A nice book Mackinnon's is a good one. But I would say it's a bit more difficult in terms of math and depth of expanations than Greene's one. Nevetheless, that's my choice! | ||
Much Better Than Green's In Terms of Quality and Price. 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. | ||
No one like this 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. | ||
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); | ||