Customer Reviews

A Guide to Econometrics

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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.

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.

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 . . . . .:-)

Peter Kennedy's writing is such a pleasure to read. He makes Econometrics so clear by focusing on intuition and attempts to avoid confusing mathematical notation. This is a book that every economist, graduate student in economics, and anyone interested in doing empirical research MUST have. This is the best book in econometrics out there to get introduced to the field or to refresh forgotten concepts. I suggest that if you are struggling with a specific econometric concept, use Kennedy first and then move on to your assigned textbook (e.g. Greene).

Absolutely the best... I chanced upon an antiquated clothbound edition in the Dewey liby while preparing for the 1.202J mid-term and that was definitely one of the luckiest days in my (grad student) life... the fifth edition is a mega-improvement over that relic... especially with the addition of a chapter on Bayesian methods... what is great about this book is what another review(er) has said before this review: the structure of each 3-part chapter (basics; general notes; technical notes) is along the lines of the preacher explaining (in a Rudyard Kipling piece (?)) the efficacy of his sermons -- something to the tune of "First I tells them what I'm gonna tell them, then I tells them, then I tells them what I told them." You will find it it a breeze flipping through just the first parts of each chapter in order to clear the cobwebs. Very very highly recommended!!!

Why essential?

1) Focuses on the main ideas of econometrics, not the nasty formulas.
2) Provides an educated opinion about recent trends in the field.
3) Lists the things you absolutely must know how to do.

I highly recommend this book as a source of intuition for econometrics. As a Ph.D. student working on my own research I find this book very helpful when I want a quick and easy explanation. This book is also good for clarifying some basic concepts that never got adequate explanations in my econometrics courses. I only wish that this book had a little more coverage on limited dependent variable models.

I am an MBA who works with many PhDs in Economics. This book has been very helpful in understanding the key principles of econometrics, so I can have meaningful discussions about what the right tool for the job is, what their implicit assumptions and drawbacks are, and how we make sure we are evaluating causality correctly. Highly recommended!

This book explains in details the intuitive meaning of econometrics.
This is the only book i know that makes you understand the subject, instead of showing how smart the author is in deriving asymptotical equations, proving theorems, etc.

If you are learning for the first time, read this book together with any "classical" econometric book. If you are an analyst, read it simply as a refresher while modelling a "real" problem.

You cant go wrong.

This is a great econometrics book. I wish that I had found this book earlier in my graduate career, and now I find myself going to it all the time. It covers all the important concepts and is very clearly written. The best thing about the book is that it teaches how to use econometrics not just what it is. It makes that very important jump of teaching students how to apply these tools properly. I cannot recommend this book enough!