The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy

8 Pages Posted: 3 Jan 2006 Last revised: 30 Dec 2011

See all articles by J. Scott Armstrong

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Abstract

Using findings from empirically-based comparisons, Scott Armstrong develops nine generalizations that can improve forecast accuracy. He finds that these are often ignored by organizations, so that attention to them offers substantial opportunities for gain. In this paper, Scott offers recommendations on how to structure a forecasting problem, how to tap managers' knowledge, and how to select appropriate forecasting methods.

Keywords: forecast method selection, domain knowledge, decomposition, judgmental bootstrapping, causal models, simulated interaction, combining forecasts

JEL Classification: CO, C2

Suggested Citation

Armstrong, J. Scott, The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy. International Journal of Applied Forecasting, Vol. 1, pp. 29-35, June 2005. Available at SSRN: https://ssrn.com/abstract=868496

J. Scott Armstrong (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-5087 (Phone)
215-898-2534 (Fax)

HOME PAGE: http://marketing.wharton.upenn.edu/people/faculty/armstrong.cfm

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