A Guide to Econometrics - 4th Edition [Paperback]

Peter E. Kennedy (Author)

Book Description

ISBN-10: 0262611406 | ISBN-13: 978-0262611404 | Publication Date: May 8, 1998 | Edition: 4

A Guide to Econometrics has established itself as the first-choice text for teachers and students throughout the world. It provides an overview of the subject and an intuitive feel for its concepts and techniques without the notation and technical detail often characteristic of econometrics textbooks. The fourth edition updates the contents and references thoughout, while retaining the basic structure and flavor of earlier editions. New material has been added on several topics, such as bootstrapping, count data, duration models, generalized method of moments, instrumental variable estimation, linear structural relations, Monte Carlo studies, neural nets, time series analysis, and VARs. A new appendix and a new type of exercise underline the importance of the sampling distribution concept.

Editorial Reviews

Review

"Peter Kennedy's book, which provides intuitive, narrative explanations for a wide range of topics covered in undergraduate and graduate econometrics courses, occupies a unique position in the econometrics textbook market."
—David Ribar, Department of Economics, the George Washington University

About the Author

The late Peter E. Kennedy was Professor Emeritus of Economics at Simon Fraser University.

Product Details

• Paperback: 468 pages
• Publisher: The MIT Press; 4 edition (May 8, 1998)
• Language: English
• ISBN-10: 0262611406
• ISBN-13: 978-0262611404
• Product Dimensions: 8.9 x 5.9 x 1 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 4.9 out of 5 stars  See all reviews (14 customer reviews)
• Amazon Best Sellers Rank: #649,114 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

143 of 146 people found the following review helpful:

5.0 out of 5 stars Best "Intuition" for and Explaination of Econometrics, May 10, 2000

By A Customer

This review is from: A Guide to Econometrics - 4th Edition (Paperback)

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.

34 of 34 people found the following review helpful:

5.0 out of 5 stars Where were you when I needed you!, November 15, 2000

By 

Charlie Adams (Washington, DC) - See all my reviews

This review is from: A Guide to Econometrics - 4th Edition (Paperback)

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.

24 of 25 people found the following review helpful:

5.0 out of 5 stars basic text on econometrics, January 23, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: A Guide to Econometrics - 4th Edition (Paperback)

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.