Ebook: Analysis of Integrated and Cointegrated Time Series with R
Author: Dr. Bernhard Pfaff (auth.)
- Tags: Statistical Theory and Methods, Econometrics, Probability and Statistics in Computer Science, Probability Theory and Stochastic Processes
- Series: Use R!
- Year: 2008
- Publisher: Springer-Verlag New York
- Edition: 2
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xx
Univariate Analysis of Stationary Time Series....Pages 3-21
Multivariate Analysis of Stationary Time Series....Pages 23-51
Non-stationary Time Series....Pages 53-71
Cointegration....Pages 73-87
Testing for the Order of Integration....Pages 91-105
Further Considerations....Pages 107-118
Single-Equation Methods....Pages 121-127
Multiple-Equation Methods....Pages 129-159
Back Matter....Pages 161-188
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.
Content:
Front Matter....Pages i-xx
Univariate Analysis of Stationary Time Series....Pages 3-21
Multivariate Analysis of Stationary Time Series....Pages 23-51
Non-stationary Time Series....Pages 53-71
Cointegration....Pages 73-87
Testing for the Order of Integration....Pages 91-105
Further Considerations....Pages 107-118
Single-Equation Methods....Pages 121-127
Multiple-Equation Methods....Pages 129-159
Back Matter....Pages 161-188
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