Features
- Cover Type: Paperback with 200 pages
- Published by: Springer
- Edition: 2nd Edition August 11, 2008
- Written in: English
- ISBN 10 Number: 0387759662
- ISBN 13 Number: 978-0387759661
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Book Dimensions:
9.2 x 6.1 x 0.5 inches
- Weighs: 11.2 ounces
Product Review
From the reviews:
“All in all, it is a book by which the usage of R for analyzing time series with the mentioned tools will surely be inhanced. It is hoped that the series expands further with similar well done texts.” (Allgemeines Statistisches Archiv, 90:3, pgs 486-487)
“Topics in stationary and non-stationary time series, together with their application to univariate and multivariate analyses are covered in this book. … The author explains how easily the methods and tools can be implemented in R – the open-source statistical programming environment. Exercises are provided … and give the reader an opportunity to apply the presented tests and methods to previously published data sets. The text is suitable for private study but would provide an great course companion to computer-based laboratory classes.” (C.M. O’Brien, Short Book Reviews, 26:2, 2006)
“I would recommend this book as a handy reference. It tersely presents the basic ideas in integrated or cointegrated analysis of time series and provides easily understandable examples of R code in implementing those examples.” (Jane L. Harvill, Journal of the American Statistical Association, 102:477, 2007)
“A welcome addition – both for econometricians and non-econometricians – as it stimulates creative research in disciplines outside economics and sharing of code in this area through the CRAN project. … Some examples with real data are also presented. … The exercises are more applied … and use interesting data sets. The bibliography is very useful. … I highly recommend this book.” (Juana Sanchez, Journal of Applied Statistics, 34:8, 2007)
--This text refers to an out of print or unavailable edition of this title.
Product Description
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.
Reader Reviews
This review is from: Analysis of Integrated and Co-integrated Time Series with R (Use R) (Paperback)
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.
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