- Taschenbuch: 236 Seiten
- Verlag: Springer; Auflage: 2008 (28. August 2008)
- Sprache: Englisch
- ISBN-10: 0387773169
- ISBN-13: 978-0387773162
- Größe und/oder Gewicht: 15,5 x 1,4 x 23,5 cm
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- Amazon Bestseller-Rang: Nr. 284.203 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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Applied Econometrics with R (Use R!) (Englisch) Taschenbuch – 28. August 2008
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Researchers in quantitative social sciences in general, and econometrics in particular, have often favored scripting languages such as GAUSS or Stat, or packages such as EViews. Introducing R to this particular audience could therefore be a well-appreciated title among the growing number of publications about R…. So, is this a good introduction of R for econometricians? Absolutely― with a well-rounded selection of available methodologies, both classic and current, and a good focus on introducing graphical methods, as well as gently covering more novel and therefore less familiar approaches, it fulfills its task with aplomb. The writing style is conversational without being shallow. (Dirk Eddelbuettel, Journal of Statistical Software, February 2009, Vol. 29, Book Review 14)
This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN). It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care.The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike. Alle Produktbeschreibungen
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+ All the data required to work the samples and exercises is contained either in the AER R-package, which was specifically designed for the book, or other standard R packages. There is no need, like with so many other books an R, to download files from the web or set them up manually.
+ Presentation of content is superb! The text in the book is extremely well written and easy to understand for beginners. The code in R works 100% (even today, 7 years after the book was published) and is placed perfectly in the text. Every line of code used to generate plots or outputs is actually in the book, which is probably due to the fact that the authors used R itself to compile the book (using Sweave()), thus showing not only rigor but also the immense capabilities of R as text-formatting tool.
+ End of chapter exercises are set up perfectly. In many other books on R, end of chapter exercises are either not solvable just by reading the text or force the reader to crawl the web for right answers. In AER, exercises actually build on the knowledge presented in the preceding chapter and are fun.
+ Text editing is on the spot. I could only find two minor typos in the text!
Certainly, every book has its flaws, but AER's flaws do not force me to subtract any stars for the final rating. In my opinion, AER only has two minor flaws. Firstly, my concern is that the first introductory chapter, in which the authors well meaningly showcase the capabilities of R, can be off-putting to novice R users, as many of the R arguments showcased are either very special or are never explained later on. Secondly, writing this review in 2015 on a book that came out in 2008, I cannot shake the feeling that a second revised edition is overdue. I do not mean to say that the methods presented are outdated, but since 2008, many new packages have become standard in R that are not/barely mentioned in AER (e.g., lme4, xts, VAR, and rugarch).
All in all, AER is the perfect book for everyone who wants to apply the theoretical knowledge from dusty tomes like Greene or simply wants a concise "advanced regression 101".