Nottingham in 1959 where later he held the Chair of Applied Statistics and Econometrics. He was a Harkness Fellow at Priceton in 1959–1960 and has also been a visitor at Stanford, Vienna, Canberra and elsewhere. He has published seven books and over 90 articles on forecasting, time series analysis, spectral analysis, econometrics, speculative markets and pricing theory. He has been Professor of Economics, University of California, San Diego since 1976 and is currently Chair of Economics. He is also Fellow of the Econometric Society and associate editor of several journals, including Applied Economics, Energy, Journal of Econometrics, Journal of the American Statistical Association, Business and Economic Statistics and Journal of Financial Economics.
Improved methods of combining forecasts
Article first published online: 21 SEP 2006
Copyright © 1984 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 3, Issue 2, pages 197–204, April/June 1984
How to Cite
Granger, C. W. J. and Ramanathan, R. (1984), Improved methods of combining forecasts. J. Forecast., 3: 197–204. doi: 10.1002/for.3980030207
- Issue published online: 21 SEP 2006
- Article first published online: 21 SEP 2006
- Manuscript Revised: MAY 1983
- Manuscript Received: JUL 1982
- ARMA models;
It is well known that a linear combination of forecasts can outperform individual forecasts. The common practice, however, is to obtain a weighted average of forecasts, with the weights adding up to unity. This paper considers three alternative approaches to obtaining linear combinations. It is shown that the best method is to add a constant term and not to constrain the weights to add to unity. These methods are tested with data on forecasts of quarterly hog prices, both within and out of sample. It is demonstrated that the optimum method proposed here is superior to the common practice of letting the weights add up to one.