Essays in econometrics : collected papers of Clive W.J. Granger. Volume 2, Causality, integration and cointegration, and long memory / edited by Eric Ghysels, Norman R. Swanson, Mark W. Watson. [electronic resource]

Format:

Book

Author/Creator:

Granger, C. W. J. (Clive William John), 1934-2009, author.

Contributor:

Ghysels, Eric, 1956- editor.

Swanson, Norman R. (Norman Rasmus), 1964- editor.

Watson, Mark W., editor.

Series:

Econometric Society monographs ; 33.

Econometric Society monographs ; 33

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (xviii, 378 pages) : digital, PDF file(s).

Edition:

1st ed.

Place of Publication:

Cambridge : Cambridge University Press, 2001.

Summary:

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.

Contents:

Cover

Half-title

Series-title

Title

Copyright

Dedication

Contents

Acknowledgments

ACADEMIC PRESS

AMERICAN STATISTICAL ASSOCIATION

BLACKWELL PUBLISHERS

BUREAU OF THE CENSUS

CAMBRIDGE UNIVERSITY PRESS

CHARTERED INSTITUTION OF WATER AND ENVIRONMENTAL MANAGEMENT

THE ECONOMETRICS SOCIETY

ELSEVIER

FEDERAL RESERVE BANK OF MINNEAPOLIS

HELBING AND LICHTENHAHN VERLAG

JOHN WILEY &amp

SONS, LTD.

MACMILLAN PUBLISHERS, LTD.

MIT PRESS

TAYLOR &amp

FRANCIS, LTD.

Contributors

Introduction

Volume I

SPECTRAL METHODS

SEASONALITY

NONLINEARITY

METHODOLOGY

FORECASTING

Volume II

CAUSALITY

INTEGRATION AND COINTEGRATION

LONG MEMORY

REFERENCES

PART ONE CAUSALITY

CHAPTER 1 Investigating Causal Relations by Econometric Models and Cross-Spectral Methods

I. SPECTRAL METHODS

II. FEEDBACK MODELS

III. CAUSALITY

IV. TWO-VARIABLE MODELS

V. THREE-VARIABLE MODELS

VI. CONCLUSION

CHAPTER 2 Testing for Causality

1. THE PROBLEM AND A DEFINITION

2. A VARIETY OF VIEWPOINTS ON CAUSALITY

3. AN OPERATIONAL DEFINITION

4. SOME DIFFICULTIES

5. TEST PROCEDURES

6. DISCUSSION AND CONCLUSIONS

CHAPTER 3 Some Recent Developments in A Concept of Causality

1. INTRODUCTION

2. CO-INTEGRATION AND CAUSATION

3. INSTANTANEOUS CAUSALITY

4. CAUSALITY AND CONTROL VARIABLES

CHAPTER 4 Advertising and Aggregate Consumption: An Analysis of Causality

2. PREVIOUS STUDIES

3. TESTING FOR CAUSALITY

4. THE DATA

5. EMPIRICAL RESULTS

6. CONCLUSIONS

APPENDIX

PART TWO INTEGRATION AND COINTEGRATION

CHAPTER 5 Spurious Regressions in Econometrics

2. SOME RESULTS IN TIME SERIES ANALYSIS

3. HOW NONSENSE REGRESSIONS CAN ARISE.

4. SOME SIMULATION RESULTS

5. DISCUSSION AND CONCLUSION

CHAPTER 6 Some Properties of Time Series Data and Their Use in Econometric Model Specification

2. INTEGRATED SERIES AND FILTERS

3. THE ALGEBRA OF INTEGRATED SERIES AND IT'S IMPLICATIONS

4. CO-INTEGRATED SERIES

5. CONCLUSION

CHAPTER 7 Time Series Analysis of Error-Correction Models

2. THE ONE-WAY CAUSAL MODEL

3. MULTI-COMPONENT CO-INTEGRATED SERIES

4. THE BIVARIATE FEEDBACK CASE

5. AGGREGATION

6. TESTING FOR CO-INTEGRATION

7. APPLICATION 1: EMPLOYEES' INCOME AND NATIONAL INCOME

8. APPLICATION 2. M3 AND GNP

9. APPLICATION 3. PRICES, WAGES AND PRODUCTIVITY IN THE TRANSPORTATION INDUSTRY

10. CONCLUSIONS

APPENDIX 1. FRACTIONAL INTEGRATED SERIES

APPENDIX 2. ERROR CORRECTION AND SEASONALITY

CHAPTER 8 Co-Integration and Error Correction: Representation, Estimation, and Testing

2. INTEGRATION, CO-INTEGRATION, AND ERROR CORRECTION

3. PROPERTIES OF CO-INTEGRATED VARIABLES AND THEIR REPRESENTATIONS

4. ESTIMATING CO-INTEGRATED SYSTEMS

5. TESTING FOR CO-INTEGRATION

6. EXAMPLES

7. CONCLUSION

CHAPTER 9 Developments in the Study of Cointegrated Economic Variables

2. COINTEGRATION

3. TESTING FOR COINTEGRATION

4. GENERALISATION: MANY VARIABLES AND GENERAL COINTEGRATION

5. FURTHER GENERALIZATIONS

6. CONCLUSION

CHAPTER 10 Seasonal Integration and Cointegration

2. SEASONAL TIME-SERIES PROCESSES

3. TESTING FOR SEASONAL UNIT ROOTS

4. ERROR-CORRECTION REPRESENTATION

5. TESTING FOR COINTEGRATION: AN APPLICATION

CHAPTER 11 A Cointegration Analysis of Treasury Bill Yields

2. THEORETICAL FRAMEWORK.

A. Theory of the Term Structure

B. Integration and Cointegration within the Term Structure

C. Modeling Cointegrated Data

3. THE DATA

4. THE EMPIRICAL EVIDENCE

A. Time Series Properties of Individual Yields

B. Cointegration Analysis

C. Error Correction Models

D. Forecasts

CHAPTER 12 Estimation of Common Long-Memory Components in Cointegrated Systems

1. FACTOR MODEL

2. ESTIMATION AND TESTING

3. APPLICATIONS

3.1 Consumption and GNP, Dividends and Stock Prices

3.2 Interest Rates in Canada and the United States

4. CONCLUSION

ACKNOWLEDGMENTS

APPENDIX: PROOFS OF THE MAIN RESULTS

CHAPTER 13 Separation in Cointegrated Systems and Persistent-Transitory Decompositions

2. DEFINITION OF THE CONCEPTS

2.1 Notions of Separation in Cointegrated Systems

2.2 P-T Decomposition of a Vector Time Series

3. PERSISTENT-TRANSITORY DECOMPOSITION IN SEPARATED COINTEGRATING SYSTEMS

3.1 Erroneously Treating Non- and Partially-separated Systems as Completely Separated

3.2 Partial Separation and P-T Decomposition of the Full System

4. EXTENSIONS TO NON-LINEAR ERROR CORRECTION MODELS

CHAPTER 14 Nonlinear Transformations of Integrated Time Series

2. UNIT ROOT TESTS ON TRANSFORMED SERIES

3. COINTEGRATED VARIABLES

4. CONCLUSIONS

ACKNOWLEDGEMENTS

CHAPTER 15 Long Memory Series with Attractors

2. SHORT AND LONG MEMORY

3. BIVARIATE ATTRACTOR

4. ESTIMATION OF THE ATTRACTOR

5. TESTING FOR AN ATTRACTOR

6. AN APPLICATION

CHAPTER 16 Further Developments in the Study of Cointegrated Variables

2. SIMPLE GENERALIZATIONS

3. NONLINEAR GENERALIZATIONS

4. CURRENT INTERPRETATIONS.

5. EXAMPLE OF NONLINEAR ERROR-CORRECTION

6. EARLY WARNINGS, FRAGILITY AND THE FUTURE

PART THREE LONG MEMORY

CHAPTER 17 An Introduction to Long-Memory Time Series Models and Fractional Differencing

1. ON DIFFERENCING TIME SERIES

2. TIME SERIES PROPERTIES

4. FORECASTING AND ESTIMATION OF d

5. PRACTICAL EXPERIENCE

APPENDIX: THE d = 0 CASE

CHAPTER 18 Long Memory Relationships and the Aggregation of Dynamic Models

2. AGGREGATION OF INDEPENDENT SERIES

3. AGGREGATION OF DEPENDENT SERIES

4. SOME OTHER MODELS

CHAPTER 19 A Long Memory Property of Stock Market Returns and a New Model

2. THE DATA

3. AUTOCORRELATION ANALYSIS OF THE RETURN SERIES

4. SENSITIVITY OF AUTOCORRELATION STRUCTURE

5. MONTE-CARLO STUDY OF VARIOUS FINANCIAL TIME SERIES MODELS

6. A NEW MODEL - ASYMMETRIC POWER ARCH

APPENDIX A

APPENDIX B. CONDITIONS FOR THE EXISTENCE OF…

Index.

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and index.

ISBN:

1-139-88285-6

1-280-16035-7

1-139-14681-5

0-511-11903-8

0-511-06725-9

0-511-06094-7

0-511-29763-7

0-511-75397-7

0-511-06938-3

OCLC:

62887014