Essays in Econometrics: Collected Papers of Clive W. J. Granger

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

Overview

These are econometrician Clive W. J. Granger's major essays in spectral analysis, seasonality, nonlinearity, methodology, and forecasting.

Product Details

ISBN-13:	9780521774963
Publisher:	Cambridge University Press
Publication date:	07/23/2001
Series:	Econometric Society Monographs , #32
Edition description:	New Edition
Pages:	544
Product dimensions:	6.02(w) x 9.09(h) x 1.14(d)

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.

Editorial Reviews

"The book is highly recommended as a reference for researchers on many important topics such as forecasting, non-linearity, causality, co-integration and long-memory. And it can also serve as a resource for applications of time series modeling to econometrics for practitioners." Mathematical Reviews

From the Publisher