A Guide to Econometrics, 5th Edition

This book was used as a text for one of my courses, and Kennedy does a strong job of explaining basic econometrics in really clear language. It's definitely an old standby, and it's useful for trying to figure out lots of statistics in a very good theoretical manner.As somebody who is pretty weak in statistics, this book definitely gave some very clear explanations of a whole slew of statistical methods for analyzing data sets, so I'd say it's useful even to non-econometricians as well as its intended audience of econometricians.

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.

One the best books on econometrics.

excellent

Loving this book, very easy to understand

a very good nontechnical book. kennedy does a great job of providing insight into an often obscure subject. excellent companion book to go along with a more technical book like Greene or Hayashi.

Not many scientists can write but Peter Kennedy is NOT one of them. He presents the mathematical and statistical information in clear, concise language. A wonderful AND informative read!!

Very useful. Like the layout.