Analysis of Integrated and Cointegrated Time Series with R

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 fr...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Pfaff, Bernhard
التنسيق: Livre numérique
اللغة:Anglais
منشور في: New York, NY : Springer New York 2008.
Cham : Springer Nature
سلاسل:Use R!
الموضوعات:
الوصول للمادة أونلاين:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
ملاحظة: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Variante du titre:R-code for examples in the book
Edition sous un autre format:• Analysis of integrated and cointegrated time series with R, Bernhard Pfaff, Second edition, 2008, [New York], Springer, 1 vol. (XX-188 p.), Use R!, 978-0-387-75966-1
• Analysis of Integrated and Cointegrated Time Series with R, Texte imprimé, 9780387567570
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505 0 |a Theoretical Concepts -- Univariate Analysis of Stationary Time Series -- Multivariate Analysis of Stationary Time Series -- Non-stationary Time Series -- Cointegration -- Unit Root Tests -- Testing for the Order of Integration -- Further Considerations -- Cointegration -- Single-Equation Methods -- Multiple-Equation Methods. 
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520 |a 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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