Identification of Asymmetric Prediction Intervals Through Causal Forces

Journal of Forecasting, Vol. 20, No. 4, pp. 273-283, July 2001

10 Pages Posted: 7 Feb 2005 Last revised: 22 Jul 2008

See all articles by J. Scott Armstrong

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Fred Collopy

Case Western Reserve University - Department of Information Systems

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.

Keywords: Forecasting, asymmetric prediction, causal forces, series forecast

Suggested Citation

Armstrong, J. Scott and Collopy, Fred, Identification of Asymmetric Prediction Intervals Through Causal Forces. Journal of Forecasting, Vol. 20, No. 4, pp. 273-283, July 2001, Available at SSRN: https://ssrn.com/abstract=662601

J. Scott Armstrong (Contact Author)

University of Pennsylvania - Marketing Department ( email )

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Fred Collopy

Case Western Reserve University - Department of Information Systems ( email )

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Cleveland, OH 44106-7235
United States