Essentials of Econometrics

I gave this book 5 stars and wanted to post a response of what it is good for and what it is not good for. I am writing from the point of view of someone who has been a practitioner, teacher, and a student, all in recent times. 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 o

Gujarati has written a series of textbooks on econometrics, and I have found each of them to be useful. In the middle '70's, when econometric methods were catching on in a big way in sociology and political science, many of us felt as if we had been reduced to premature obsolescence: econometrics, whatever it's virtues, was typically presented in a densely mathematical fashion which made it inaccessible to non-specialists. The 1975 version of Gujarati's text, however, was a real help in making econometrics interpretable. This text is written in the same accessible way, and it's coverage of important topics has kept pace with developments in the literature. Econometrics is never easy reading, but Gujarati has done it about as well as can be done. My only difficulty with this text is that the author spends a great deal of time with simple regression, introducing assumptions, tests for violations of assumptions, and possible remedies. His account would be more interesting if he got to multiple regression, using interesting examples, a good deal earlier than he does. This could be accomplished without departing from Gujarati's early emphasis on introduction of requirements of the classical linear regression model.

Gujarati's BASIC ECONOMETRICS uses divide and conquer method to help readers achieve a solid grasp of the basics of econometrics. Each chapter takes on a major concept and explains it thouroughly. As the name suggests, this is basic econometrics and hence does not make much use of matrix algebra and other mathematical tools often used in advanced econometrics. However, all the statistical and mathematical concepts used are included in the appendix. If one's aim is to prepare for graduate level econometrics, then a more advanced text would be recommended. Otherwise, this book is the right way to enter the world of econometrics.

It's the best book out there for understanding basis statistics and its application to economics. It is very easy to read, understand and relatively easy to teach considering the intensity of the subject.

This book certainly has its good points; it is written in a non-technical manner which allows everyone the ability to grasp technical material; it also presents statistics thorougly before it introduces Econometrics.As an professor, the major weakness is that there are not enough applications in areas such as forecasting; there were also errors in the first edition which needed to be corrected, and there was perhaps too much emphasis in teaching Statistics(Some of it is irrelevant toEconometrics). However, I would certainly recommend it.