Causality, integration and cointegration, and long memory

Author(s)

• Granger, C. W. J. (Clive William John)
• Ghysels, Eric
• Swanson, Norman R. (Norman Rasmus)
• Watson, Mark W.

Bibliographic Information

Causality, integration and cointegration, and long memory

Clive W.J. Granger ; edited by Eric Ghysels, Norman R. Swanson, Mark W. Watson

（Econometric Society monographs, no. 33 . Essays in econometrics : collected papers of Clive W.J. Granger / edited by Eric Ghysels, Norman R. Swanson, Mark W. Watson ; v. 2）

Cambridge University Press, 2001

• : set, hbk
• : set, pbk
• : hbk
• : pbk

Note

Includes bibliographical references and index

Description and Table of Contents

Volume

: hbk ISBN 9780521792073

Description

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Table of Contents

• Part I. Causality: 1. Investigating causal relations by econometric models and cross-spectral methods
• 2. Testing for causality
• 3. Some recent developments in a concept of causality
• 4. Advertising and aggregate consumption: an analysis of causality R. Ashley and R. Schmalensee
• Part II. Integration and Cointegration: 5. Spurious regressions in econometrics
• 6. Some properties of time series data and their use in econometric model specification
• 7. Time series analysis of error correction models A. A. Weiss
• 8. Co-Integration and error-correction: representation, estimation and testing
• 9. Developments in the study of cointegrated economic variables
• 10. Seasonal integration and cointegration S. Hylleberg, R. F. Engle and B. S. Yoo
• 11. A cointegration analysis of Treasury Bill yields A. D. Hall and H. M. Anderson
• 12. Estimation of common long-memory components in Cointegrated Systems J. Gonzalo
• 13. Separation in cointegrated systems and persistent-transitory decompositions N. Haldrup
• 14. Nonlinear transformations of Integrated Time Series J. Hallman
• 15. Long Memory Series with attractors J. Hallman
• 16. Further developments in the study of cointegrated variables N. R. Swanson
• Part III. Long Memory: 17. An introduction to long-memory Time Series models and fractional differencing R. Joyeux
• 18. Long-memory relationships and the aggregation of dynamic models
• 19. A long memory property of stock market returns and a new model Z. Ding and R. F. Engle.

Volume

: pbk ISBN 9780521796491

Description

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Table of Contents

• Part I. Causality: 1. Investigating causal relations by econometric models and cross-spectral methods
• 2. Testing for causality
• 3. Some recent developments in a concept of causality
• 4. Advertising and aggregate consumption: an analysis of causality R. Ashley and R. Schmalensee
• Part II. Integration and Cointegration: 5. Spurious regressions in econometrics
• 6. Some properties of time series data and their use in econometric model specification
• 7. Time series analysis of error correction models A. A. Weiss
• 8. Co-Integration and error-correction: representation, estimation and testing
• 9. Developments in the study of cointegrated economic variables
• 10. Seasonal integration and cointegration S. Hylleberg, R. F. Engle and B. S. Yoo
• 11. A cointegration analysis of Treasury Bill yields A. D. Hall and H. M. Anderson
• 12. Estimation of common long-memory components in Cointegrated Systems J. Gonzalo
• 13. Separation in cointegrated systems and persistent-transitory decompositions N. Haldrup
• 14. Nonlinear transformations of Integrated Time Series J. Hallman
• 15. Long Memory Series with attractors J. Hallman
• 16. Further developments in the study of cointegrated variables N. R. Swanson
• Part III. Long Memory: 17. An introduction to long-memory Time Series models and fractional differencing R. Joyeux
• 18. Long-memory relationships and the aggregation of dynamic models
• 19. A long memory property of stock market returns and a new model Z. Ding and R. F. Engle.

Volume

: set, pbk ISBN 9780521796972

Description

This two-volume set of books in the Econometric Society Monographs series (ESM numbers 32 and 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in these volumes explore topics in spectral analysis, seasonality, nonlinearity, methodology, forecasting, causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Table of Contents

• Volume I: Introduction to Volumes I and II
• 1. A profile: the ET Interview: Professor Clive Granger
• Part I. Spectral Analysis: 2. Spectral analysis of New York Stock Market prices O. Morgenstern
• 3. The typical spectral shape of an eonomic variable
• Part II. Seasonality: 4. Seasonality: causation, interpretation and implications A. Zellner
• 5. Is seasonal adjustment a linear or nonlinear data-filtering process? E. Ghysels and P. L. Siklos
• Part III. Nonlinearity: 6. Non-linear Time Series Modeling A. Anderson
• 7. Using the correlation exponent to decide whether an economic series is chaotic T. Liu and W. P. Heller
• 8. Testing for neglected nonlinearity in Time Series Models: a comparison of neural network methods and alternative tests
• 9. Modeling nonlinear relationships between extended-memory variables
• 10. Semiparametric estimates of the relation between weather and electricity sales R. F. Engle, J. Rice and A. Weiss
• Part IV. Methodology: 11. Time Series Modeling and interpretation M. J. Morris
• 12. On the invertibility of Time Series Models A. Anderson
• 13. Near normality and some econometric models
• 14. The Time Series approach to econometric model building P. Newbold
• 15. Comments on the evaluation of policy models
• 16. Implications of aggregation with common factors
• Part V. Forecasting: 17. Estimating the probability of flooding on a tidal river
• 18. Prediction with a generalized cost of error function
• 19. Some comments on the evaluation of economic forecasts P. Newbold
• 20. The combination of forecasts
• 21. Invited review: combining forecasts - twenty years later
• 22. The combination of forecasts using changing weights M. Deutsch and T. Terasvirta
• 23. Forecasting transformed series
• 24. Forecasting white noise A. Zellner
• 25. Can we improve the perceived quality of economic forecasts? Short-run forecasts of electricity loads and peaks R. Ramanathan, R. F. Engle, F. Vahid-Araghi and C. Brace. Volume II: Part I. Causality: 1. Investigating causal relations by econometric models and cross-spectral methods
• 2. Testing for causality
• 3. Some recent developments in a concept of causality
• 4. Advertising and aggregate consumption: an analysis of causality R. Ashley and R. Schmalensee
• Part II. Integration and Cointegration: 5. Spurious regressions in econometrics
• 6. Some properties of time series data and their use in econometric model specification
• 7. Time series analysis of error correction models A. A. Weiss
• 8. Co-Integration and error-correction: representation, estimation and testing
• 9. Developments in the study of cointegrated economic variables
• 10. Seasonal integration and cointegration S. Hylleberg, R. F. Engle and B. S. Yoo
• 11. A cointegration analysis of Treasury Bill yields A. D. Hall and H. M. Anderson
• 12. Estimation of common long-memory components in Cointegrated Systems J. Gonzalo
• 13. Separation in cointegrated systems and persistent-transitory decompositions N. Haldrup
• 14. Nonlinear transformations of Integrated Time Series J. Hallman
• 15. Long Memory Series with attractors J. Hallman
• 16. Further developments in the study of cointegrated variables N. R. Swanson
• Part III. Long Memory: 17. An introduction to long-memory Time Series models and fractional differencing R. Joyeux
• 18. Long-memory relationships and the aggregation of dynamic models
• 19. A long memory property of stock market returns and a new model Z. Ding and R. F. Engle.

Volume

: set, hbk ISBN 9780521804073

Description

This two-volume set of books in the Econometric Society Monographs series (ESM numbers 32 and 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in these volumes explore topics in spectral analysis, seasonality, nonlinearity, methodology, forecasting, causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Table of Contents

• Volume I: Introduction to Volumes I and II
• 1. A profile: the ET Interview: Professor Clive Granger
• Part I. Spectral Analysis: 2. Spectral analysis of New York Stock Market prices O. Morgenstern
• 3. The typical spectral shape of an eonomic variable
• Part II. Seasonality: 4. Seasonality: causation, interpretation and implications A. Zellner
• 5. Is seasonal adjustment a linear or nonlinear data-filtering process? E. Ghysels and P. L. Siklos
• Part III. Nonlinearity: 6. Non-linear Time Series Modeling A. Anderson
• 7. Using the correlation exponent to decide whether an economic series is chaotic T. Liu and W. P. Heller
• 8. Testing for neglected nonlinearity in Time Series Models: a comparison of neural network methods and alternative tests
• 9. Modeling nonlinear relationships between extended-memory variables
• 10. Semiparametric estimates of the relation between weather and electricity sales R. F. Engle, J. Rice and A. Weiss
• Part IV. Methodology: 11. Time Series Modeling and interpretation M. J. Morris
• 12. On the invertibility of Time Series Models A. Anderson
• 13. Near normality and some econometric models
• 14. The Time Series approach to econometric model building P. Newbold
• 15. Comments on the evaluation of policy models
• 16. Implications of aggregation with common factors
• Part V. Forecasting: 17. Estimating the probability of flooding on a tidal river
• 18. Prediction with a generalized cost of error function
• 19. Some comments on the evaluation of economic forecasts P. Newbold
• 20. The combination of forecasts
• 21. Invited review: combining forecasts - twenty years later
• 22. The combination of forecasts using changing weights M. Deutsch and T. Terasvirta
• 23. Forecasting transformed series
• 24. Forecasting white noise A. Zellner
• 25. Can we improve the perceived quality of economic forecasts? Short-run forecasts of electricity loads and peaks R. Ramanathan, R. F. Engle, F. Vahid-Araghi and C. Brace. Volume II: Part I. Causality: 1. Investigating causal relations by econometric models and cross-spectral methods
• 2. Testing for causality
• 3. Some recent developments in a concept of causality
• 4. Advertising and aggregate consumption: an analysis of causality R. Ashley and R. Schmalensee
• Part II. Integration and Cointegration: 5. Spurious regressions in econometrics
• 6. Some properties of time series data and their use in econometric model specification
• 7. Time series analysis of error correction models A. A. Weiss
• 8. Co-Integration and error-correction: representation, estimation and testing
• 9. Developments in the study of cointegrated economic variables
• 10. Seasonal integration and cointegration S. Hylleberg, R. F. Engle and B. S. Yoo
• 11. A cointegration analysis of Treasury Bill yields A. D. Hall and H. M. Anderson
• 12. Estimation of common long-memory components in Cointegrated Systems J. Gonzalo
• 13. Separation in cointegrated systems and persistent-transitory decompositions N. Haldrup
• 14. Nonlinear transformations of Integrated Time Series J. Hallman
• 15. Long Memory Series with attractors J. Hallman
• 16. Further developments in the study of cointegrated variables N. R. Swanson
• Part III. Long Memory: 17. An introduction to long-memory Time Series models and fractional differencing R. Joyeux
• 18. Long-memory relationships and the aggregation of dynamic models
• 19. A long memory property of stock market returns and a new model Z. Ding and R. F. Engle.

by "Nielsen BookData"

Details

• NCID: BA54743228
• ISBN:
  • 0521804078
  • 0521796970
  • 052179207X
  • 0521796490
• LCCN: 00034306
• Country Code: uk
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cambridge ; New York
• Pages/Volumes: xviii, 378 p.
• Size: 23 cm
• Classification:
  • LCC : HB139
  • DC21 : 330/.01/5195
• Subject Headings:
  • LCSH : Econometrics
• Parent Bibliography ID:
  • BA00183651