Simple Versus Complex Forecasting: The Evidence

27 Pages Posted: 15 Aug 2015

See all articles by Kesten C. Green

Kesten C. Green

University of South Australia - UniSA Business School; Ehrenberg-Bass Institute for Marketing Science

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Date Written: March 1, 2015

Abstract

This article introduces the Special Issue on simple versus complex methods in forecasting. Simplicity in forecasting requires that (1) method, (2) representation of cumulative knowledge, (3) relationships in models, and (4) relationships among models, forecasts, and decisions are all sufficiently uncomplicated as to be easily understood by decision-makers. Our review of studies comparing simple and complex methods — including those in this special issue — found 97 comparisons in 32 papers. None of the papers provide a balance of evidence that complexity improves forecast accuracy. Complexity increases forecast error by 27 percent on average in the 25 papers with quantitative comparisons. The finding is consistent with prior research to identify valid forecasting methods: all 22 previously identified evidence-based forecasting procedures are simple. Nevertheless, complexity remains popular among researchers, forecasters, and clients. Some evidence suggests that the popularity of complexity may be due to incentives: (1) researchers are rewarded for publishing in highly ranked journals, which favor complexity; (2) forecasters can use complex methods to provide forecasts that support decision-makers’ plans; and (3) forecasters’ clients may be reassured by incomprehensibility. Clients who prefer accuracy should accept forecasts only from simple evidence-based procedures. They can rate the simplicity of forecasters’ procedures using the questionnaire at the simple-forecasting website.

Keywords: analytics; big data; decision-making; decomposition; econometrics; Occam’s razor.

JEL Classification: C5

Suggested Citation

Green, Kesten C. and Armstrong, J. Scott, Simple Versus Complex Forecasting: The Evidence (March 1, 2015). Available at SSRN: https://ssrn.com/abstract=2643534 or http://dx.doi.org/10.2139/ssrn.2643534

Kesten C. Green (Contact Author)

University of South Australia - UniSA Business School ( email )

GPO Box 2471
Adelaide, SA 5001
Australia
+61 8 83012 9097 (Phone)

HOME PAGE: http://people.unisa.edu.au/Kesten.Green

Ehrenberg-Bass Institute for Marketing Science ( email )

Australia

HOME PAGE: http://www.marketingscience.info/people/KestenGreen.html

J. Scott Armstrong

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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