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Home > Catalogue > Essays in Econometrics
Essays in Econometrics


  • 33 b/w illus. 76 tables
  • Page extent: 544 pages
  • Size: 228 x 152 mm
  • Weight: 0.89 kg

Library of Congress

  • Dewey number: 330/.01/5195
  • Dewey version: 21
  • LC Classification: HB139 .G69 2001
  • LC Subject headings:
    • Econometrics
    • Bilingualism
    • Ovid,--43 B.C.-17 or 18 A.D.--Influence
    • English literature--Roman influences

Library of Congress Record

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 (ISBN-13: 9780521772976 | ISBN-10: 0521772974)

DOI: 10.2277/0521772974

Manufactured on demand: supplied direct from the printer

 (Stock level updated: 17:00 GMT, 06 October 2015)


This book, and its companion volume in the Econometric Society Monographs series (ESM number 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 this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

• Major essays of arguably the world's leading active econometrician • Granger is internationally known, author of 1999 Press title Empirical Modeling in Economics • Topics cover major areas of econometrics and time series analysis, including forecasting, seasonality, and nonlinearity


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.


Eric Ghysels, Norman R. Swanson, Mark W. Watson, A. Zellner, P. L. Siklos, A. Anderson, T. Liu, W. P. Heller, R. F. Engle, J. Rice, A. Weiss, M. J. Morris, P. Newbold, M. Deutsch, T. Terasvirta, R. Ramanathan, F. Vahid-Araghi, C. Brace

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