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.
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.
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