Analysis of Integrated and Cointegrated Time Series with R

Beschreibung

The analysis of integrated and cointegrated 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 cointegration 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 autoregressive, structural vector autoregressive, 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.

Bernhard Pfaff studied economics at the universities of Göttingen, Germany; Davis, California; and Freiburg im Breisgau, Germany. He obtained a diploma and a doctorate degree at the economics department of the latter entity where he was employed as a research and teaching assistant. He has worked for many years as economist and quantitative analyst in research departments of financial institutions and he is the author and maintainer of the contributed R packages "urca" and "vars."

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.

Ideally suited for computer labs: Econometric theory/methods and their implementation within R is exhibited

Self-contained: The book can be used for self-study; code examples are elaborated

Wide audience is addressed: Upper-undergraduate/Graduate students and practitioners

This is the only book devoted to the main methodology employed in applied econometrics: the analysis of integrated and co-integrated time series. Beyond introducing the core concepts, the text shows how to conduct the various unit root tests and co-integration methods using the free statistical programming environment R. In addition, the book encompasses seasonal unit roots, fractional integration, coping with structural breaks and inference in co-integrated vector autoregressive models, and includes numerous programming examples involving artificial and real data.

Autor*in

Bernhard Pfaff

Themen in »Analysis of Integrated and Cointegrated Time Series with R«

Co-integration SVEC models VAR VEC Variance calculus cointegration econometrics fractional integration time series unit roots value at risk value-at-risk

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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 excellent 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)

“The first edition of this book was released in 2006. The format and intention remains the same, however … new topics have been added. 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 that work in this area…” (Technometrics)

“The book introduces mainly procedures from the R package urca which is maintained by the author. … It gives an overview of a range of procedures which provide a good basis for a unit root and cointegration analysis. … In summary, the book can be useful for someone who is familiar with R and wants to write R code for unit root and cointegration analysis.” (Helmut Lütkepohl, Statistical Papers, Vol. 52, 2011)

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