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A Course in Econometrics Hardcover – May 15, 1991

ISBN-13: 978-0674175440 ISBN-10: 0674175441

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Product Details

  • Hardcover: 432 pages
  • Publisher: Harvard University Press (May 15, 1991)
  • Language: English
  • ISBN-10: 0674175441
  • ISBN-13: 978-0674175440
  • Product Dimensions: 6.1 x 1 x 9.2 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 3.6 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #401,489 in Books (See Top 100 in Books)

Editorial Reviews

Review

This book is an excellent choice for first year graduate econometrics courses because it provides a solid foundation in statistical reasoning in a manner that is both clear and concise. It addresses a number of issues that are of central importance to developing practitioners and theorists alike and achieves this in a fairly nontechnical manner...The topics addressed here are rarely given such a thorough treatment in econometrics textbooks. For example, in discussions of bivariate distributions, Goldberger points out that two uncorrelated normal random variables may not be independent, since a nonnormal bivariate distribution can generate normal marginal distributions. Other texts typically leave readers with the impression that two uncorrelated normal random variables are independent without reference to their joint distribution...A Course in Econometrics is rigorous, it makes students think hard about important issues, and it avoids a cookbook approach. For these reasons, I strongly recommend it as a basic text for all first year graduate econometrics courses. (Douglas G. Steigerwald Econometric Theory)

[A Course in Econometrics] strike[s] the right balance between mathematical rigour and intuitive feel. It aims to prepare students for empirical research but also those who go on to more advanced econometrics...The book is very clear and very precise. It is built on just a few very simple concepts. I think that students will like it very much. I congratulate Professor Goldberger with having written a very useful book. (Jan R. Magnus Economic Journal)

Undoubtedly the best Ph.D. level econometrics textbook available today. The analogy principle of estimation serves to unify the treatment of a wide range of topics that are at the foundation of empirical economics. The notation is concise and consistently used throughout the text...Students have expressed delight in unraveling the proofs and lemmas. It's a pleasure to teach from this book. Recommended for any serious economics student or anyone interested in studying the principles underlying applied economics. (Michael Hazilla, American University)

About the Author

Arthur S. Goldberger was Professor of Economics, Emeritus, at the University of Wisconsin-Madison.

Customer Reviews

3.6 out of 5 stars
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Most Helpful Customer Reviews

11 of 11 people found the following review helpful By Akop Pogosian on November 30, 2008
Format: Hardcover Verified Purchase
This is an exceptionally well-written text on introductory econometrics suitable for self-study or for use in an advanced undergraduate or a first-year graduate-level course in econometrics. The only prerequisites for reading this text are a good understanding of freshman calculus and a working knowledge of basic matrix algebra. The necessary probability theory and statistics knowledge are developed in the text. This book is less than 400 pages long and a motivated reader can read this text from cover to cover in a few weeks. This is not a very high price to pay to gain a solid understanding of the most fundamental tools of cross-sectional econometrics. The emphasis is on developing the core tools used in econometrics. This text is very readable but at the same time fairly concise. Compare this to many other texts that are padded with hundreds of pages of empirical examples and other verbal detours from the core material. The author is never too verbose, but at the same time offers helpful explanations and examples in cases where the reader is likely to be confused. These days, most graduate econometrics courses are taught from other, more modern, and supposedly more advanced econometrics texts. While many of those popular graduate-level econometrics texts cover significantly more material, they also read like a terrible train-wreck of badly assembled subjects that are extremely difficult to digest on the first (and sometimes second) reading and specially on your own. Therefore, even an advanced graduate student who was once confused by those text may benefit from reading Goldberger's text.

Approximately one third of the text is devoted to the background knowledge in statistics and probability. The second part of the text develops the classical normal linear model.
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8 of 8 people found the following review helpful By A Customer on May 15, 1999
Format: Hardcover
This textbook for advanced undergraduates or first-year graduate students leads the reader through the basic concepts and statistical procedures of econometrics. The extensive use of matrix notation allows the reader to easily apply Goldberger's formulae to actual data (especially with computer packages such as Gauss and Matlab). Although the author gives brief treatments which sometimes require several readings to be fully understood, he is by no means unclear or confusing. Quite the contrary, as the book is very concise. Sections which I found especially useful and interesting were: 22.5 Statistical versus Economic Significance, 23 Multicollinearity (actually humorous!), and 24 Regression Strategies.
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7 of 7 people found the following review helpful By Amazon Customer on November 11, 2005
Format: Hardcover
This is a good introduction to econometrics, appropriate for the first semester of a 1st year graduate course, or maybe for an advanced undergraduate course. It is written in a relatively straighforward and dry way, but I found it clear and to the point. Goldberger also differs from other textbooks because he seems more able than most authors to provide an intuition (even if brief) for several concepts. Many of the exercises are also very useful, and well thought-out. The math is not forbidding, and is adequate to a first year PhD in economics, and maybe even a bit too simple for that.

It is a bit dated, and it does not cover anything new or semi-new in econometrics, but admittedly this book should only be an introduction, so it does its job fine. Just take into account that learning the content of this book will not be enough to be a good practictioner, so if you are self-teaching the topic to yourself you should probably look at other books, such as Wooldridge's Econometrics analysis of cross section and panel data.

The one thing I hated about the book is the notation. The author keeps introducing new notation page after page (aiming at providing "shortcuts" for longer expressions), and the new notation builds on the previous one. The result is that when you go back to this book to use it as a reference and you need a concept in, say, chapter 10, you have to go back to chapter 7 for some notation, and this will send you back to chapter 3 and so on and so forth. Overall, though, I think this is a very good book, from a very distinguished author.
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6 of 6 people found the following review helpful By A Customer on September 5, 1999
Format: Hardcover
This is one of those rare textbooks that manages to be both a good introduction to the subject and a useful reference. Readable on the first try and well organized. Highly recommended.
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7 of 8 people found the following review helpful By Amazon Customer on March 17, 2010
Format: Hardcover Verified Purchase
I would give this book 6 stars if I could. I think James Heckman has called this book a masterpiece, and I would fully agree. This is a unique text that takes a distinctive approach -- one which, in my opinion, is essential for really understanding econometrics. I am a PhD in economics, and I explored a great many econometrics texts in my quest to get a better handle on the subject. I found that while I can follow all the proofs in standard texts like Greene, I didn't really get the intuition behind how things worked. All that changed once I picked up Goldberger. Goldberger takes what I would call the "identification" approach (an approach emphasized by other well regarded econometricians such as Heckman and Manski). The identification part of econometrics is the link between a model and the probability distribution function of observed variables. If you had an infinitely large sample, so you knew the joint probability distribution exactly, how does that help you identify some interesting parameter in your model? Secondary to this is the issue that in real life, you have only finite samples, and you estimate parameters of the joint pdf only with uncertainty. This is where standard errors and confidence intervals come in. But the identification part is really the core part of econometrics, and is very simple. Most econometrics texts mix identification and estimation, and so unnecessarily confuse the issue. For instance, in the standard approach, the fact that OLS estimates are biased when there is measurement error in the independent variable is usually directly proved by algebraically manipulating the OLS estimator. But this can be seen in the identification part alone, without any reference directly to the OLS estimator.Read more ›
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