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.
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.
For a book of this size, he covers a lot of territory. He covers the CLR model and hypothesis testing well, and discusses a few other things too. This guide is hardly encyclopedic. However, it covers the things economists need to know most.
Kennedy does more than just explain econometrics. He spells out the limits of econometric analysis. Texts often pay little attention to the 'con in econometrics'. Not Kennedy. He discusses the limitations and defects in standard techniques, as well as their advantages.
The only thing wrong with this book is that it does not carry the reader along far enough. After reading this book, most reader's will likely move on to a standard (i.e. badly written) econometrics textbook. In contrast, this book is written so well that it almost makes learning econometrics fun!