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.