A Guide to Econometrics

by Peter E. Kennedy

Book Overview

A Guide to Econometrics has established itself as a preferred 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 that characterize most econometrics textbooks.

The fifth edition has two major additions, a chapter on panel data and an innovative chapter on applied econometrics. Existing chapters have...

• Format: Paperback
• Language: English
• ISBN: 026261183X
• ISBN13: 9780262611831
• Release Date: January 2003
• Publisher: MIT Press
• Length: 500 Pages
• Weight: 3.75 lbs.
• Dimensions: 1.1" x 6.0" x 9.2"
• Age Range: 18 years and up
• Grade Range: Postsecondary and higher

Customer Reviews

well written classic on econometrics

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.

Best "Intuition" for and Explaination of Econometrics

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.

Great book for those wanting a layman's understanding.

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.

Non-Optional Reading for ANYONE involved in econometrics.

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.

a great supplement to any good text

This book doesn't pretend to be a text. It answers the questions a text usually doesn't: like how does it all fit together? and what do we do when our assumptions don't hold? The author points out that econometrics texts are usually "cookbooks", and aim to tell you how to use their collection of receipes. This book explains the assumptions behind regression, and what happens when we violate them, and what we can do about it. It also gives a good overview of the topic. This book shows the connection between the receipes in a text. It doesn't pretend to teach econometrics, so the reader isn't innundated with equations. Instead, you get a discussion of why you would want to do (or not do) the things your text would teach you. Anyone who is taking an econometrics course (graduate or undergraduate) should get this book. After reading a chapter in your text, read the corresponding chapter in Kennedy to add some depth to your understanding. I would also suggest this to graduate students who are facing preliminary or comprehensive exams in econometrics. This will help you to "bring it all together", and answer those vague, conceptual questions which seem to cause the most grief. The book has sufficient references to the literature so that you can easily follow up anything you want to explore in more depth, but it's clear and self-contained enough that only an econometrician is likely to feel the need. Each chapter is organized in three parts: a discussion of the topic, an appendix of long footnotes which add details which would obstruct the flow of the discussion, and finally, a technical appendix, where you can find a few of those equations if you really want them. This makes it easy to read the book at the level you need.