Marketing Papers

Document Type

Technical Report

Date of this Version


Publication Source

Journal of Business Research





Start Page


Last Page





This article introduces this JBR 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 the incomprehensability. 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

Copyright/Permission Statement

Originally published in the Journal of Business Research © 2015 Elsevier

This is a pre-publication version. The final version is available at


analytics, big data, decision-making, decomposition, econometrics, Occam's razor

Embargo Date


Available for download on Saturday, September 01, 2018



Date Posted: 15 June 2018

This document has been peer reviewed.