Some excerpts from the introduction:

"Economists are frequently interested in relationships between different quantities, for example between individual wages and the level of schooling. The most important job of econometrics is to quantify these relationships on the basis of available data and using statistical techniques, and to interpret, use or exploit the resulting outcomes appropriately. Consequently, econometrics is the interaction of economic theory, observed data and statistical methods. It is the interaction of these three that makes econometrics interesting, challenging and, perhaps, difficult. In the words of a seminar speaker, several years ago: `Econometrics is much easier without data'.
Traditionally econometrics has focussed upon aggregate economic relationships. Macro-economic models consisting of several up to many hundreds equations were specified, estimated and used for policy evaluation and forecasting. The recent theoretical developments in this area, most importantly the concept of cointegration, have generated increased attention to the modelling of macro-economic relationships and their dynamics, although typically focussing on particular aspects of the economy. Since the 1970s econometric methods are increasingly employed in micro-economic models describing individual, household or firm behavior, stimulated by the development of appropriate econometric models and estimators which take into account problems like discrete dependent variables and sample selection, by the availability of large survey data sets, and by the increasing computational possibilities. More recently, the empirical analysis of financial markets has required and stimulated many theoretical developments in econometrics. Currently econometrics plays a major role in empirical work in all fields of economics, almost without exception, and in most cases it is no longer sufficient to be able to run a few regressions and interpret the results. As a result, introductory econometrics textbooks usually provide insufficient coverage for applied researchers. On the other hand, the more advanced econometrics textbooks are often too technical or too detailed for the average economist to grasp the essential ideas and to extract the information that is needed. Thus there is a need for an accessible textbook that discusses the recent and relatively more advanced developments."
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"The number of econometric techniques that can be used is numerous and their validity often depends crucially upon the validity of the underlying assumptions. This book attempts to guide the reader through this forest of estimation and testing procedures, not by describing the beauty of all possible trees, but by walking through this forest in a structured way, skipping unnecessary side-paths, stressing the similarity of the different species that are encountered, and by pointing out dangerous pitfalls. The resulting walk is hopefully enjoyable and prevents the reader from getting lost in the econometric forest."

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"In most chapters a variety of empirical illustrations is provided in separate sections or subsections. While it is possible to skip these illustrations essentially without losing continuity, these sections do provide important aspects concerning the implementation of the methodology discussed in the preceding text. In addition, I have attempted to provide illustrations that are of economic interest in itself, using data that are typical for current empirical work and covering a wide range of different areas. This means that most data sets are used in recently published empirical work and are fairly large, both in terms of number of observations and number of variables. Given the current state of computing facilities, it is usually not a problem to handle such large data sets empirically.
Learning econometrics is not just a matter of studying a textbook. Hands on experience is crucial in the process of understanding the different methods and how and when to implement them. Therefore, readers are strongly encouraged to get their hands dirty and to estimate a number of models using appropriate or inappropriate methods, and to perform a number of alternative specification tests. With modern software becoming more and more user-friendly, the actual computation of even the more complicated estimators and test statistics is often surprisingly simple, sometimes dangerously simple. That is, even with the wrong data, the wrong model and the wrong methodology, programs may come up with results that are seemingly all right. At least some expertise is required to prevent the practitioner from such situations and this book plays an important role in this.
To stimulate the reader to use actual data and estimate some models, almost all data sets used in this text are available through the internet via the K.U.Leuven page http://www.econ.kuleuven.be/GME. Readers are encouraged to re-estimate the models reported in this text and check whether their results are the same, as well as to experiment with alternative specifications or methods. Some of the exercises make use of the same or additional data sets and provide a number of specific issues to consider."