v. 1. Spectral analysis, seasonality, nonlinearity, methodology, and forecasting -- v. 2. Causality, integration and cointegration, and long memory.

Volume 1. Part I. Spectral Analysis: 1. Spectral analysis of New York Stock Market prices O. Morgenstern; 2. The typical spectral shape of an eonomic variable; Part II. Seasonality: 3. Seasonality: causation, interpretation and implications A. Zellner; 4. Is seasonal adjustment a linear or nonlinear data-filtering process? E. Ghysels and P. L. Siklos; Part III. Nonlinearity: 5. Non-linear time series modeling A. Anderson; 6. Using the correlation exponent to decide whether an economic series is chaotic T. Liu and W. P. Heller; 7. Testing for neglected nonlinearity in time series models: a comparison of neural network methods and alternative tests; 8. Modeling nonlinear relationships between extended-memory variables; 9. Semiparametric estimates of the relation between weather and electricity sales R. F. Engle, J. Rice and A. Weiss; Part IV. Methodology: 10. Time series modeling and interpretation M. J. Morris; 11. On the invertibility of time series models A. Anderson; 12. Near normality and some econometric models; 13. The time series approach to econometric model building P. Newbold; 14. Comments on the evaluation of policy models; 15. Implications of aggregation with common factors; Part V. Forecasting: 16. Estimating the probability of flooding on a tidal river; 17. Prediction with a generalized cost of error function; 18. Some comments on the evaluation of economic forecasts P. Newbold; 19. The combination of forecasts; 20. Invited review: combining forecasts - twenty years later; 21. The combination of forecasts using changing weights M. Deutsch and T. Terasvirta; 22. Forecasting transformed series; 23. Forecasting white noise A. Zellner; 24. 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; Index.

Volume 2 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.