# A Guide to Econometrics. 6th edition, by Peter Kennedy

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This is the perfect (and essential) supplement for all econometrics classes–from a rigorous first undergraduate course, to a first master’s, to a PhD course.

- Explains what is going on in textbooks full of proofs and formulas
- Offers intuition, skepticism, insights, humor, and practical advice (dos and don’ts)
- Contains new chapters that cover instrumental variables and computational considerations
- Includes additional information on GMM, nonparametrics, and an introduction to wavelets

- Sales Rank: #198732 in Books
- Published on: 2008-02-19
- Original language: English
- Number of items: 1
- Dimensions: 9.60″ h x 1.20″ w x 7.40″ l, 2.80 pounds
- Binding: Paperback
- 598 pages

Review

“The exceptional success of this work is due to its clarity and economy of expression and the accessibility of the subject matter to a broad range of scholars. Now in its sixth edition, this guide brings practitioners and researchers up to date on the popular techniques in estimation. It holds a unique position among econometric texts. Highly recommended.” (Choice, November 2008)

From the Back Cover

This is the perfect (and essential) supplement for all econometrics classes–from a rigorous first undergraduate course, to a first master’s, to a PhD course. It explains what is going on in textbooks full of proofs and formulas. Kennedy’s A Guide to Econometrics offers intuition, skepticism, insights, humor, and practical advice (dos and don’ts). The sixth edition contains new chapters on instrumental variables and on computation considerations, more information on GMM and nonparametrics, and an introduction to wavelets.

About the Author

Peter Kennedy is Professor of Economics at Simon Fraser University. In addition to A Guide to Econometrics, he is author of Macroeconomic Essentials: Understanding Economics in the News, 2e (2000), and is Associate Editor of the International Journal of Forecasting, the Journal of Economic Education, and Economics Bulletin.

Most helpful customer reviews

55 of 59 people found the following review helpful.

A good overview of the subject

By Dr. Lee D. Carlson

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.

30 of 31 people found the following review helpful.

A semi-technical history of 40 years of econometrics

By not a natural

Throughout the 1970’s, big-name sociologists with impeccable methodological and statistical credentials sought to persuade the discipline’s journeymen that they should learn econometrics. The two most influential proponents of this view were the social statistics luminaries Hubert Blalock and Otis Dudley Duncan. Blalock was more optimistic than Duncan with regard to the ultimate payoff, but Duncan was more arrogantly dismissive of those who failed to heed his admonition.

In response, sociology and related social science journals became much more densely quantitative. Many social scientists, as a result, felt as if they had been reduced to obslescence. After all, econometrics and the other new quantitative tools, especially path analysis, which had come to dominate the discipline were difficult topics under the best of circumstances, and most social scientists lacked the mathematical training to tackle the best known econometrics texts, such as those by Jack Johnston, Jan Kmenta, and Arthur Goldberger. Many social scientists had been introduced to the econometrics mainstay, regression analysis, but not in this highly technical form.

Fortunately, the decade of the ’70’s also saw publication of Damodar Gujarati’s introductory econometrics text, as well as the first edition of Peter Kennedy’s Guide to Econometrics. Gujarati’s book presented much the same material as his more insistently mathematical colleagues, but in a much more accessible form. His book could actually be used for self-instructional purposes, enabling less methodologically astute social scientists to finally figure out what was going on.

Kennedy’s book was a forest-for-the-trees antidote to the mathematically dense and detailed texts, a book that enabled social scientists and other readers to identify topics that were of central importance and those that were ancillary details.

As with Gujarati, Kennedy wrote in accessible language and provided motivated readers with an overview of econometrics, enabling them to see what all the fuss was about. By including general notes and technical notes at the end of each chapter, Kennedy assured that his book was of value not not only to those of us who were less mathematically favored, but to those for whom use of econometrics was an everyday activity, one they had pretty well mastered.

In additon, while the first edition of Kennedy’s book ran 175 pages, the most recent (sixth) edition is a full 575 pages. This reflects the fact that, while the book continues to provide an accessible overview of econometrics, it is also a comprehensive catalogue of regression analysis correctives. Kennedy explicitly acknowledges that his objective is to compile an accessible repository of the rapidly growing list of tests and procedures available to make regression analysis more generally applicable and informative. Anyone interested in the history of econometrics over the past forty years would do well to begin with Kenndy’s book.

Even for those of us for whom this stuff does not come easily, Kennedy’s text is an invaluable reference. For the newcomer, it remains a fine overview of econometrics and a useful adjunct to any basic text. When the seventh edition comes out, it will be interesting to see what Kennedy makes of the near-obsessive concern with instrumental variable methods of causal analysis as presented, for example, in Angrist and Pischke’s Mostly Harmless Econometrics.

As an addendum, Peter Kennedy is no longer with us, so unless his publisher recruits a co-author, I assume there will be no seventh edition. Finding a co-author as dedicated, knowledgeable, and who writes as well as Kennedy would be a tough job.

14 of 14 people found the following review helpful.

College students NEED to buy this book

By Richardt

I’m a college student and recently got accepted in a master’s degree in Economics. One of the problems I had regarding books about Econometrics is the lack of mathematical and economic concept discussions. Most of them are merely mathematical proofs without any clear explanation. On the other hand, this book discusses WHY econometricians need to watch out for violations of assumptions in the linear regressions, how to detect them, and finally, how to solve them. If you want mathematical proofs I suggest you pick another book but if you really want to understand the inner workings in Econometrics, buy this book. It saved me a lot of time and headaches.

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