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Analysis of Integrated and Cointegrated Time Series with R (Use R!) (Englisch) Taschenbuch – 2. Juni 2010

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From the reviews:

"Analysis of Integrated and Cointegrated Time Series with R (2nd Edition) … offers a rigorous introduction to unit roots and cointegration, along with numerous examples in R to illustrate the various methods. The book, now in its second edition, provides an overview of this active area of research in time series econometrics. It manages to be thorough (using formal notation), yet remains applied in its focus.… The second edition also adds new material on VAR and SVAR models which strengthens the coverage of multivariate methods.… the book can clearly be recommended to both researchers and practitioners in time series econometrics." (Dirk Eddelbuettel, Journal of Statistical Software, Volume 30, Book Review 5, 2009-04-27)

"The writing is lucid and the book and software used can be recommended to its intended audience. The value of the book lies principally in showing how a number of packages including the author's own packages urca and vars may be used for modern econometric analysis." (David J. Scott, International Statistical Review, 77, 1, 2009)

From the reviews of the second edition:

"The book is divided into three parts. … This book addresses senior undergraduates, graduate students and practitioners in the field of econometrics. This is not a text in statistical theory, but does cover modern statistical methodology. It is particularly suited as an accompanying text in applied computer laboratory classes." (M. P. Moklyachuk, Mathematical Reviews, Issue 2009 k)

“The prominent feature of this book is that it demonstrates how rapidly different inference methods, diagnostic testing, impulse response analysis, forecast error variance decomposition, and forecasting can be implemented with R, which may interest many practitioners working in this arena. … the book is descriptive, some chapters are a combination of overviews and developments. This book has several programming examples that utilize both real and artificial data. The style and format of the edition is standard and it offers Name, Function and Subject indexes.” (Technometrics, Vol. 52 (1), February, 2010)


The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models.The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other.

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Von Simone am 22. September 2010
Format: Taschenbuch
It is really a difficult task to explain in a simple manner cointegration, VAR, VECM... I think this is a great practical guide providing also an extremely compressed explanation.

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Die hilfreichsten Kundenrezensionen auf (beta) 4 Rezensionen
17 von 18 Kunden fanden die folgende Rezension hilfreich
Not much there. 22. Januar 2008
Von Genevieve Hayes - Veröffentlicht auf
Format: Taschenbuch
I'm not sure whether the author's purpose in writing this text book was to create a text on time series analysis or a manual for R users, but whichever it was, he didn't succeed. This is a very slim text (especially considering how much it costs), only 139 pages in total, and not surprisingly, there's just not that much in it. Pfaff covers a reasonably large range of topics, including stationary ARMA processes, cointegration, unit root tests, etc, but only a few pages are devoted to each topic and no worked examples are provided to assist the reader's understanding (exercises are provided at the end of each chapter, but no solutions are given). As the title suggests, some R code is given to implement various techniques that are described throughout this book, but this is never annotated and you would be better off looking at one of the various R tutorials that can be obtained for free on the internet.
4 von 4 Kunden fanden die folgende Rezension hilfreich
Great Resource for cointegration in R 1. Dezember 2011
Von blutzed44 - Veröffentlicht auf
Format: Taschenbuch
This book is not meant for people who are new to R or new to multivariate time series. If you are an intermediate R programmer and have graduate level training in time series, you will find this book extremely useful. The books covers univariate, multivariate, integrated, and cointegrated time series. The authors briefly shows the theoretical properties of all the requisite models, but the author gives extensive references if the reader wishes to investigate further. The author also shows how to implement the models in R. That being said, the book does not provide enough examples. In the third edition, the author should dive deeper into the working of the R functions in the urca package. It would be nice if the author provided an extensive case study on cointegration modeling like he did in his journal of statistical software article: [...]. The author should also use more comments in his code. Sometimes it's distracting to have to read the text to determine what is going on in the code.
13 von 17 Kunden fanden die folgende Rezension hilfreich
Excellent Short Book 12. Mai 2007
Von B. Peterson - Veröffentlicht auf
Format: Taschenbuch Verifizierter Kauf
The UseR! series from Springer aims to be a series of short, focused books on implementation topics using the R statistical environment. Bernard Pfaff does a good job of presenting a good though brief overview that covers ARMA, stationarity, and cointegration. The math is not too heavy for the practitioner without a deep academic background, and the code examples are relevant and concise, demonstrating the concepts well. The book also does a reasonable job of integrating graphics into the analysis, which can greatly aid in understanding.
0 von 4 Kunden fanden die folgende Rezension hilfreich
Integración y cointegración con R 6. September 2009
Von Ruben Ibarra - Veröffentlicht auf
Format: Taschenbuch
Todos aquellos que han trabajado con algún software econométrico como Eviews o PcGive, recibiran con beneplacito el libro de Bernhard Pfaff: Analysis of Integrated and Cointegrated Time Series with R. Su utilidad para estudiantes, profesores e investigadores en el área econométrica empírica es invaluable, ya que incluye temas de relativo desarrollo reciente como los Test de Raíces unitarias bajo Cambio Estructural y Cointegración bajo Cambio Estructural, que no se encuentran implementados aún en los paquetes econométricos estándar, y son imprescindibles para el trabajo econométrico empírico actualmente.

Al estar implementado bajo la plataforma R, el practicante puede tener acceso a al código fuente de los programas, lo cual le permite utilizarlos como punto de partida para incorporar desarrollos teóricos recientes en el área econométrica, haciendo la labor de los investigadores más productiva.
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