A Guide to Econometrics [Paperback]

Peter Kennedy (Author)

Book Description

ISBN-10: 026261183X | ISBN-13: 978-0262611831 | Publication Date: August 1, 2003 | Edition: 5th

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 been revised and updated extensively, particularly the specification chapter (to coordinate with the applied econometrics chapter), the qualitative dependent variables chapter (to better explain the difference between multinomial and conditional logit), the limited dependent variables chapter (to provide a better interpretation of Tobit estimation), and the time series chapter (to incorporate the vector autoregression discussion from the simultaneous equations chapter and to explain more fully estimation of vector error correction models). Several new exercises have been added, some of which form new sections on bootstrapping and on applied econometrics.This edition is for sale in all of the Americas, the West Indies, and U.S. dependencies only.

Editorial Reviews

Review

"Peter Kennedy's Guide to Econometrics is a superb text. I first used it as a student, and now I find it indispensable as a professor. I would not dream of teaching an econometrics course without it."--Lisa A. Keister, Department of Sociology, The Ohio State University

"*A Guide to Econometrics* is an excellent book that is, or at least should be, essential reading for all undergraduate and graduate students with an interest in the subject, as well as scholars engaged in applied econometric work."--Anders Olofsgård, Georgetown University

"This revised version is a worthy successor to the fourth edition, which was already the most useful applied econometrics book on the market. It is an excellent complement to standard econometrics textbooks, which tend to be heavy on mathematics and technique, and light on practical advice."--T.H. Gindling, Department of Economics, University of Maryland Baltimore County

"This has been an excellent book in the past and the revisions for the fifth edition include some new subjects and bring the material up to date. The text is clearly written and shows the author's excellent understanding of the material covered. And his clever insights and witty comments make the book fun to read."--Jan Kmenta, Professor Emeritus of Economics, University of Michigan

About the Author

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

Product Details

• Reading level: Ages 18 and up
  
• Paperback: 500 pages
• Publisher: The MIT Press; 5th edition (August 1, 2003)
• Language: English
• ISBN-10: 026261183X
• ISBN-13: 978-0262611831
• Product Dimensions: 9.2 x 6 x 1.2 inches
• Shipping Weight: 1.9 pounds (View shipping rates and policies)
• Average Customer Review: 4.9 out of 5 stars  See all reviews (17 customer reviews)
• Amazon Best Sellers Rank: #83,918 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

48 of 52 people found the following review helpful:

4.0 out of 5 stars A good overview of the subject, April 14, 2006

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
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This review is from: A Guide to Econometrics (Paperback)

Econometrics is now a respectable topic, both in the financial industry, where it is used extensively, and in academia. Like most efforts to model phenomena in the real world, especially those that attempt to model the behavior of human agents, econometrics has had its share of critics. These critics pointed out some of the failures of the econometric models, and some of their criticism was justified. However, there have been successes as well, if one realizes that the success of a model should be determined by what a model is actually developed for.

The author of this book is fully aware of what modeling is all about, and gives a very interesting overview of the major mathematical techniques used in econometrics. He characterizes econometrics as a study of how to obtain a good estimator in a situation or problem at hand that must be estimated. He recognizes that any criteria for what is "good" is somewhat subjective, but a "good" estimator it is generally believed must be computationally cost effective, unbiased, efficient, and robust. The author gives detailed discussions of these criteria in the book, and throughout most of the book more detailed mathematical derivations take place in the notes at the end of each chapter. The discussions can be a bit wordy at times in places outside of the notes for this reason. The book includes of course discussions on least squares, nonlinear regression, and Bayesian estimation of parameters. These are all topics that are fairly standard in the literature, but the author also includes discussions on topics such as neural networks and kernel estimation. An extensive list of exercises is included at the end of the book. For practitioners, the author includes a list of "ten commandments" that should be respected when doing applied econometric analysis.

No guide on econometric techniques would be complete without a discussion on how to analyze time series, and in this one that author points out the differences between how econometricians analyze time series and how traditional time series analysts do. The arrival of studies indicating problems with the approaches of the econometricians resulted in an explosion of research activity, some of which is reviewed by the author. This includes discussions of the Box-Jenkins method, ARIMA (autoregressive integrated moving average) models, VAR (vector autoregression), and error-correction (ECM) models. Interestingly, and close to the truth in practice, the author views model selection as being an art form, the correct choice of which is highly dependent on the experience of the modeler. Also interesting is his discussion on the `structural economic time series approach' (SEMSTA), which arose when econometricians realized their methods were being outperformed by Box-Jenkins methods, and which can be described as a synthesis of the two. When SEMSTA is simplified by omitting the moving average component, one obtains the VAR model. The author discusses in some detail the controversies behind the use of VAR, due to its assumption that all variables be endogenous. Both the ARIMA and VAR models are viewed as being successful in econometrics due to their ability to deal with the dynamics of the economy, even though they ignore the role of long-run equilibria. When terms are included in these models to represent the extent to which the long-run equilibrium is not met, one obtains the error-correction models. The author discusses an explicit example of how to obtain an ECM representation when there is linear relation occurring in the long run. Embedded in all the discussions on time series is the problem on how to deal with nonstationary data, the latter of which econometricians ignored historically, due to their belief that econometric analysis was not affected by nonstationary variables, and due to the unavailability of studies that indicated that most macroeconomic data obeys a `random walk' and is therefore nonstationary.

The author also gives a brief outline of forecasting techniques in econometrics and how to assess their accuracy. He emphasizes that the choice of how to evaluate the accuracy of the forecasting model depends on the actual purpose of the forecast. If a large degree of error can be tolerated, this may motivate the choice of one criterion for accuracy over another. Unfortunately forecasting is viewed by many as an activity that should guarantee high or even infinite accuracy. Since no forecasting model can guarantee this, and since a perusal of the historical record on forecasting shows that most of them have "missed the target", forecasting is viewed with ever-increasing skepticism (this is especially true for the current controversy over climate forecasting and global warming). There needs to be an objective study that compares the accuracy of the forecasting models and which also compares their utility in prediction over and above what is typically called "intuition" or some other equally subjective ability. Other than a brief discussion on neural networks, the use of machine intelligence to do forecasting is not discussed in the book. It is becoming more popular to use artificial intelligence in forecasting, but it remains to be seen whether using it is more advantageous than simulation or Monte Carlo techniques, both of the latter being dependent essentially on randomization and requiring minimal intelligence.

14 of 15 people found the following review helpful:

4.0 out of 5 stars Cliffs Note for econometrics students and practitioners, March 6, 2006

By 

Gadgester "No Time, No Money" (Mother Earth) - See all my reviews
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This review is from: A Guide to Econometrics (Paperback)

Like the (in)famous Cliffs Notes many of us depended on to get through high school, Canadian economist Peter Kennedy's "A Guide to Econometrics" is not an econometrics textbook by itself, but rather a who-what-why-when guide for the student or practitioner mired in one too many technical details. Instead of stuffing the pages with mathematical formulas and dreaded matrix notations in bold or italics, this book gives the cliched "36,000-ft." overview of why we do certain things in econometrics. To borrow another cliche, it allows the reader to see the forest rather than getting lost among the individual trees.

To me, the last chapter "Applied Econometrics," newly added in this fifth edition of the book, alone is worth the money spent. It systematically treats the important topic of how to practice econometrics in the real world. It's not a comprehensive treatment on this topic, but an eye-opening one that drives home the important point of econometrics being a tool for answering (hopefully) relevant economic questions. For example, there's a whole section on what to do when you get the "wrong" sign on a coefficient estimate. Instead of panicking and jumping out of the window, the reader is advised to take a close, sensible (my word) look at the specifications, both of the economic ("theoretical") model and the overall econometric specification ("empirical framework") as well as the quality of data itself.

As a practitioner of econometrics, all too often I've observed people, many with Ph.D.s from top programs, blindly running regressions without considering the who-what-why-when's of econometric analysis. Case in point: how many of you routinely test for collinearity in your data? If you discover collinarity, what do you do then? Surprisingly, products of the various "top" econometric machineries across the nation often ignore these questions, or just are plain ignorant of these issues.

Real-world example (trust me): just the other day a fine gentleman with a Ph.D. from Stanford University gave a talk on his latest research. When he showed his empirical results, most in the room -- many non-Ph.D.s -- were horrified to see two variables that were obviously collinear. I mean, it was obvious to almost everyone in the audience, yet the gentleman was fully unaware of his grave error and continue to boast the advanced techniques he used to derive the estimates under different assumption -- except the most important one which he clearly had violated!

I think Kennedy's book will help students understand econometrics -- esp. the why -- better and help practitioners avoid making embarrassing mistakes like the ones I cited. Kennedy not only helps you see when and why you would use techniques like IV or SURE, but provides ample examples to show when and why you shouldn't use them. The latter, alas, is just as important as the former, and I hope every econometrician has read or will read this book before they delve into future applied research.

This book is not five-star because I find it a bit too superficial in the treatment of many important topics. I guess it's hard to cover so many topics in a small paperback like this. I hope future editions will give us more formulas and mathematical notations to make the book tie in better with existing econometric textbooks.

8 of 9 people found the following review helpful:

5.0 out of 5 stars Kennedy's Guide one of the "must haves", March 13, 2006

By 

Mark Gdovin (Denver, CO USA) - See all my reviews
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This review is from: A Guide to Econometrics (Paperback)

I purchased this book as a replacement for an earlier edition that was "borrowed" and not returned some years ago. I'm glad it wasn't as the new edition is worth having in its own right. Peter Kennedy's A Guide to Econometrics is one of those "must haves" for any applied economist or graduate student. While I think of it as a supplement or the key to gain access to much more rigorous works such as Greene's Econometric Analysis, Kennedy's is one of the few books on the topic that if you could have no other, it can get the job done by itself.

It is neither so difficult as to require a lot of pre-requisite math nor is it so simplistic to be of limited use. It is quite well balanced in that regard. My only complaint is that I wish it were hardcover (because of the use it gets) and that it was printed in a larger font in a larger book (I'm fifty and it is not the easiest to read anymore . . . . .:-)