- Hardcover: 576 pages
- Publisher: McGraw-Hill Education; 4 edition (May 12, 2009)
- Language: English
- ISBN-10: 0073375845
- ISBN-13: 978-0073375847
- Product Dimensions: 7.5 x 0.9 x 9.4 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
- Average Customer Review: 12 customer reviews
- Amazon Best Sellers Rank: #54,256 in Books (See Top 100 in Books)
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Essentials of Econometrics 4th Edition
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About the Author
After teaching for more than 28 years at the City University of New York, He is currently a professor of Economics in the Department of Social Sciences at the U.S. Military Academy at West Point, New York. Dr. Gujarati received his M.Com. degree from the University of Bombay in 1960, his M.B.A. degree from the University of Chicago in 1963, and his Ph.D. degree from the University of Chicago in 1965. Dr. Gujarati has published extensively in recognized national and international journals, such as the Review of Economics and Statistics, the Economic Journal, the Journal of Financial and Quantitative Analysis, the Journal of Business, the American Statistician, and the Journal of Industrial and Labor Relations.
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First, relative to other books on Econometrics or statistics for that manner, this is one of the easier ones to understand. If, as a student, you are having difficulty, it is most likely because you either haven't quite yet had adequate experience with the topic and would benefit for solid instruction (which is sadly lacking for many universities). In other words, the concept being taught is actually highly embedded both with research methodology and statistics. If you (or your professor) are weak on either,book or no book, this would be a tough one to learn on one's own.
Second, provided you have some background, the amazing thing about this book is that it gives you an adequate amount to ask vast majority of the "right" questions to conduct a statistical study. The sections on skew and kurtosis are better than two of the statistical texts that I have on my shelf. The sections on multi-collinearity, heteroskedacity, and auto-correlation are just enough to recognize for a beginner to understand the major statistical techniques. I admit, they ARE NOT adequate for someone who specifically needs the math side for running a very specific set of slightly more advanced techniques used in finance to address time series. However, that is not the need for most undergraduates or even many graduates; and in fact, that shouldn't be taught first anyway.
This book is NOT going to be adequate as a stand alone for someone who really needs to be advanced in this topic. Clearly, they do not address linear regression in the context of linear algebra. Similarly, the latter sections are written for a beginner and not someone who is going to do advanced modeling techniques to manage heteroskedacity, multi-colinearity, and auto-correlation.
For someone who has the right foundation to become advanced, it's a fantastic place to start, as you can kind of get a better top down sense for where your education needs to bring you. For someone who is already a stats graduate degree type person, you may want to look up texts that address linear regression and the special topics specifically. If you are a professor, you can assign a GOOD text on linear regression for your nerdier students to reference.
From a practitioner's point of view, I used this text quite a bit when I first got started. This is because the organization is fantastic. The answers are where you would expect them. For example, what do I do if my data is not normally distributed, go to skew and kurtosis and all the tests are easily laid out as well as how to interpret them and some of the alternative transformations that can be done. A lot of linear regression texts are far too weak on interpretation which is far more important to a practitioner. In other words, for other texts, they stop short at identifying the set is non-normal (graphically, not mathematically) and then transforming. They do not then tell you what to do to the transformed data.
Most recent customer reviews
the content is mess.
Have no idea why my college prof choosed this garbage as our textbook.