Customer Reviews

A Guide to Econometrics - 4th Edition

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4 star:		(2)
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Unfortunately, I found this book at the END of too many PhD courses where I was swamped by assumptions the instructor and various authors were making. If I had only read this book FIRST - and then read Green, Greene, Johnston, Goldberger, etc., I would have gotten much more out of the courses with less stress. Peter Kennedy writes the type of book that students dream of finding (and I dream of writing someday). Each chapter is in 3 parts: 1) Overview of what and why, 2) Some more detail and 3) The nitty gritty that you'll worry about in Amemiya's book and others. This is the perfect book for PhD students interested in learning econometrics as a tool instead of an area of research (i.e. developing new models). Once you learn the basics, you can go to Greene's Econometrics Analysis for the details regarding implementation. My recommendation for "reading" this book is to whip through Part 1 of each chapter for the best overview of econometrics ever. (Peter Kennedy is an excellent writer, so this is actually an enjoyable and interesting experience.) Then revisit the relevant chapters before tackling the assigned readings for your course.

This is the book that they don't tell you about to make your lives a living hell. This is the handbook that explains what the heck the econometrics professor is talking about. It should be a required companion to the Greene, Judge, Hamilton, whatever. Every time I have an econometrics question, I pick this up and the answer is inside. Last year, it was survival models. This year, it is neural networks. Both times, the book has come through with a rational explanation of what was going on.

The fact that it doesn't cost a hundred bucks like every other econometrics book seems to these days makes it that much greater. I would recommend it to any student who was beginning a PhD program in economics.

This is the fourth edition of a very popular text for an introductory graduate level course in econometrics. Although designed for econometricians and economics majors, the book has a lot to offer the statistician (time series analyst). There is good coverage of both the classical econometric models and the classical ARIMA time series models. The difference, as Kennedy points out, is that most univariate statistical time series models use only the past history to model and forecast the future while the econometric models emphasize the inclusion of economic predictor variables and not the past history.
However, in recent years, and partly because in fair-fight forecasting competitions the Box-Jenkins time series methods have done better than the econometric models, the econometricians are beginning to incorporate the Box-Jenkins approach in their models. As Kennedy points out,the new theory of multivariate ARIMA models is providing the econometricians with a methodology that is similar to their simultaneous equation models.

One nice feature of the book is that it treats classical linear regression theory early, highlights the key assumptions and then provides specific chapters that cover how to deal with the violations of the assumptions taken one by one.

The book is clear, up-to-date and has an excellent bibliography. It introduces the structural econometric time series approach along with multivariate Box-Jenkins methodolgy. Advanced topics such as dealing with roots on the unit circle in Box-Jenkins models and cointegration are covered. Also robust estimation procedures are discussed. It even introduces bootstrap methodology and the Bayesian approach to inference.

There is some coverage and some warnings about neural networks. Models for count data, duration, linear structural equations and instrumental variables are all presented in an introductory way.

Emphasis is placed early on the concept of sampling distributions for estimators. A clear understanding of sampling distributions is essential to understanding classical frequentist statistical approaches. Much confusion can arise when these concepts are glossed over.

If you are taking a course in econometrics, graduate or undergraduate, there is no good excuse for failing to purchase this book. Kennedy has a tremendous gift for giving readers a vision for the "Forest" of econometrics, which is very handy for those readers trapped in the "trees" of Pindyck & Rubinfeld and William Greene's texts (both of which are excellent, but relatively technical).

Kennedy covers an amazingly broad selection of topics in his books. While those having difficulty understanding the field will definitely get a great deal out of the book, don't think for a second that the book is overly simplistic -- an econometrics primer. No, this is not a mere review of OLS for the Gauss-Markov impaired. Kennedy's text covers Bayesian Analysis, Vector Error-Correction Models, and even touches, albeit lightly for my tastes, on such subjects as Kalman filtering and recursive least squares. Kennedy's notes are also very insightful and bring up many issues that dominant textbooks skirt around.

An essential back-up book for graduate econometrics. An elegant presentation--very few equations and no footnotes--makes it the antithesis of econometrics textbooks. This is the go-to book by which many survive basic graduate econometrics. Even after taking more advanced econometric theory courses and getting my degree, I find myself refering to it [albeit mostly in the technical notes]. The fourth edition is an improvement over the third; buy it and pass the third on to a student.

I consult to the financial industry. I often get asked to recommend a statistics/econometrics book that covers all the high-level topics, yet is understandable to lawyers and accountants. This book is not a wimp by any means. I also used the book to clarify my understanding of strengths and, more importantly, what to watch out for when applying some of the high-level econometrics. A great source book for teachers or anyone who needs to explain his/her work to laypersons.

Kennedy's Guide to Econometrics explains econometrics more clearly than any other book I have read. This book will not make you into an econometrician. But, this is definitely the place to start your education on empirical methods in economics.

For a book of this size, he covers a lot of territory. He covers the CLR model and hypothesis testing well, and discusses a few other things too. This guide is hardly encyclopedic. However, it covers the things economists need to know most.

Kennedy does more than just explain econometrics. He spells out the limits of econometric analysis. Texts often pay little attention to the 'con in econometrics'. Not Kennedy. He discusses the limitations and defects in standard techniques, as well as their advantages.

The only thing wrong with this book is that it does not carry the reader along far enough. After reading this book, most reader's will likely move on to a standard (i.e. badly written) econometrics textbook. In contrast, this book is written so well that it almost makes learning econometrics fun!

The best intuition builder in both statitics and econometrics. I have been reading the various editions throught my career. Please, keep updating it, Peter Kennedy!

If you have studied econometrics using books as Maddala, Green, Hamilton etc then this book is definetly for you. It gives a good intuitive exposition of well known methods using a non-technical language. It has lots of references to more techincally advanced books. Kennedys book will not provide you with any formulas for solving problems, this is left for the other econometric and statistic book in the market. Believe it or not, this book is actually difficult to put down ones you started reading it!

Its abolutely an excellent book. It gives the intuition of different methods and concepts without any mathematical notation. It gives a very good introduction of fundamentals- a very good way to start on any topic. A must read before starting on any traditional text book like Greene, Kmenta etc.