- Hardcover: 432 pages
- Publisher: Harvard University Press; 1st US Edition 1st Printing edition (April 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
- Average Customer Review: 12 customer reviews
- Amazon Best Sellers Rank: #86,380 in Books (See Top 100 in Books)
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A Course in Econometrics 1st US Edition 1st Printing Edition
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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.
Top customer reviews
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. The last third is devoted to various kinds of departures from the standard classical assumptions and to models such as GLS, nonlinear models, simultaneous equations, 2SLS, and 3SLS. Only this last part of the text can honestly be called "econometrics". The rest of the text is the standard material on statistical inference and linear modeling. However, this background material is at the core of most econometric tools, and Goldberger nails all issues of this background material "from A-Z". A full proof or at least a sketch of the proof is given pretty much to every result in the text.
The first 13 chapters of this textbook cover standard probability theory and statistical inference. This sets Goldberger's text aside from the rest of graduate-level introductory texts in econometrics because most of them relegate the necessary probability and statistics background into tersely written appendices. Goldberger uses some of the ideas and notation developed in those chapters later in the text, so it is useful to review the first 13 chapters even if you have studied statistics before. Chapters 7 and 18 serve as a good introduction to the bivariate and multivariate normal random variables (again, some other texts do not spend as much effort here).
Chapters 14 through 25 are devoted to meticulous development of the classical normal regression model. This is where this text truly shines. Everything is proved and explained very well. Chapter 22 and 24 are devoted to issues and strategies for empirical work. Unfortunately, most of the material in this text is developed under the assumption of non-random regressors. Chapter 25 lifts this assumption and shows that nothing really changed (except for notation). Nonetheless, I feel that it would be more in line with the spirit of econometrics to assume random regressors from the beginning. The large sample results of the least squares are stated but not proved, which is unfortunate. Given the asymptotics machinery already developed in the text, presenting a sketch of large sample proofs would not take too much space.
The rest of the text talks about GLS, nonlinear models, and simultaneous equations. The presentation of the simultaneous equations model in the subsequent chapters is very thorough with many examples. Most emphasis is on the 2-equation supply and demand type of models.
Finally, yet another interesting feature that sets this text apart is that the author emphasizes throughout it the link between OLS, conditional expectation, and best linear predictors. Many other texts barely mention this simple insight.
Unfortunately, the material on maximum likelihood is very brief and sketchy. Therefore, it is best to use some other text for MLE theory and models. There is also nothing on panel data models or GMM. I will give this text four stars. It is hard to give five stars to an basic econometrics text that does not have a chapter on standard panel data models.
To recap, the best features of this text are:
- Short, concise, yet very readable and suited for self-study.
- A brief, reasonably rigorous, but intuitive development of the necessary probability and statistics material.
- A very good analysis of the classical normal linear model.
- Good introduction to analysis of stationary time series, GLS, and SEM.
- Emphasizes the link between OLS, conditional expectation functions, and best linear predictors.
The weak points are:
- No panel data models.
- No GMM.
- MLE sections are brief and sketchy.
- Large sample theory for OLS.
But for students wishing to learn the material this book is garbage. The author doesn't bother to explain anything, and the examples given are few in number and extremely narrow in focus. Exercise problems have little or no relevance to the chapters they belong to, and often other books or sources are required just to decipher the meaning of the questions or to borrow intuition from to solve them. Prose is extremely terse, ineffective, and incomplete. It seems as though the author just didn't care enough to make the book readable or usable. Goldberg makes the reader have to work excessively hard to gain any kind of interesting or important information. This book appears to just be another example of a badly written book in an entire field of badly written material.