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Research Article
Identification of asymmetric prediction intervals through causal forces
Article first published online: 25 JUL 2001
DOI: 10.1002/for.794
Copyright © 2001 John Wiley & Sons, Ltd.
1099-131X/asset/cover.gif?v=1&s=9c5c7e58eaaad51e6136554684ff8c6d0edb1574 "Journal of Forecasting")
Additional Information
How to Cite
Armstrong, J. S. and Collopy, F. (2001), Identification of asymmetric prediction intervals through causal forces. Journal of Forecasting, 20: 273–283. doi: 10.1002/for.794
Publication History
- Issue published online: 25 JUL 2001
- Article first published online: 25 JUL 2001
- Manuscript Accepted:
- Manuscript Received:
Funded by
- US Navy Personnel R&D Center
- Office of Naval Research. Grant Number: N00014-92-J-1544
- Abstract
- References
- Cited By
Keywords:
- causal forces;
- contrary series;
- domain knowledge;
- extrapolation;
- M-Competition;
- prediction intervals
Abstract
When causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six-year-ahead forecasts, 89% of the forecasts were in the expected direction. The asymmetries were often substantial. Contrary series should be flagged and treated separately when prediction intervals are estimated, perhaps by shifting the interval in the direction of the causal forces. Copyright © 2001 John Wiley & Sons, Ltd.