Preface to the Second Edition
A little more than two years have passed since the first edition. During this
time, R has gained further ground in the domain of econometrics. This is wit-
nessed by the 2006 useR! conference in Vienna, where many sessions were
devoted entirely to econometric topics, as well as the Rmetrics workshop at
Meielisalp 2007. A forthcoming special issue of the Journal of Statistical Soft-
ware will be devoted entirely to econometric methods that have been imple-
mented within R. Furthermore, numerous new packages have been contributed
to CRAN and existing ones have been improved; a total of more than 1200
are now available. To keep up with these pleasant changes, it is therefore nec-
essary not only to adjust the R code examples from the first edition but also
to enlarge the book’s content with new topics.
However, the book’s skeleton and intention stays unchanged, given the
positive feedback received from instructors and users alike. Compared with
the first edition, vector autoregressive (VARs) models and structural vector
autoregressive (SVARs) models have been included in an entire new chapter
in the first part of the book. The theoretical underpinnings, definitions, and
motivation of VAR and SVAR models are outlined, and the various methods
that are applied to these kinds of models are illustrated by artificial data sets.
In particular, it is shown how swiftly different estimation principles, inference,
diagnostic testing, impulse response analysis, forecast error variance decom-
position, and forecasting can be conducted with R. Thereby the gap to vec-
tor error-correction models (VECMs) and structural vector error-correction
(SVEC) models is bridged. The former models are now introduced more thor-
oughly in the last chapter of the first part, and an encompassing analysis in
the context of VEC/SVEC modeling is presented in the book’s last chapter.
As was the case for the first edition, all R code examples presented can be
downloaded from http://www.pfaffikus.de.
As with the first edition, I would like to thank the R Core Team for
providing such a superb piece of software to the public and to the numerous
package authors who have enriched this software environment. I would further
like to express my gratitude to the anonymous referees who have given good
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