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.