Essays in Econometrics 2 Volume Paperback Set

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.

Spis treści:
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.