Granger C.W.J. et al. Essays in Econometrics: Spectral analysis, seasonality, nonlinearity, methodology, and forecasting

Cambridge University Press – 2001, 544 pages
ISBN: 9780521772976This 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.Part I. Spectral Analysis:
Spectral analysis of New York Stock Market prices O. Morgenstern
The typical spectral shape of an eonomic variablePart II. Seasonality:
Seasonality: causation, interpretation and implications A. Zellner
Is seasonal adjustment a linear or nonlinear data-filtering process? E. Ghysels and P. L. SiklosPart III. Nonlinearity:
Non-linear time series modeling A. Anderson
Using the correlation exponent to decide whether an economic series is chaotic T. Liu and W. P. Heller
Testing for neglected nonlinearity in time series models: a comparison of neural network methods and alternative tests
Modeling nonlinear relationships between extended-memory variables
Semiparametric estimates of the relation between weather and electricity sales R. F. Engle, J. Rice and A. WeissPart IV. Methodology:
Time series modeling and interpretation M. J. Morris
On the invertibility of time series models A. Anderson
Near normality and some econometric models
The time series approach to econometric model building P. Newbold
Comments on the evaluation of policy models
Implications of aggregation with common factorsPart V. Forecasting:
Estimating the probability of flooding on a tidal river
Prediction with a generalized cost of error function
Some comments on the evaluation of economic forecasts P. Newbold
The combination of forecasts
Invited review: combining forecasts - twenty years later
The combination of forecasts using changing weights M. Deutsch and T. Terasvirta
Forecasting transformed series
Forecasting white noise A. Zellner
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.