Analysis of Integrated and Co-integrated Time Series with R

Univariate analysis of stationary time series.- Multivariate analysis of stationary time series.- Non-stationary time series.- Cointegration.- Testing for the order of integration.- Further considerations.- Single equation methods.- Multiple equation methods.- Appendix.- Abbreviations, nomenclature and symbols.- List of tables.- List of figures.- List of R code.- References.

Bernhard Pfaff, Dr., studierte Volkswirtschaftslehre in Göttingen, Davis (Kalifornien) und Freiburg im Breisgau, wo er mit einer ökonometrischen Arbeit promoviert wurde. Nach verschiedenen Stationen als Volkswirt und quantitativer Analyst ist er heute Direktor bei Invesco Asset Management in Frankfurt.

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