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Simple versus complex forecasting: The evidence

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  • Green, Kesten C.
  • Armstrong, J. Scott

Abstract

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 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 simple-forecasting.com.

Suggested Citation

  • Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
  • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1678-1685
    DOI: 10.1016/j.jbusres.2015.03.026
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    Cited by:

    1. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    2. repec:lrk:eeaart:35_2_9 is not listed on IDEAS
    3. repec:eee:joreco:v:31:y:2016:i:c:p:174-181 is not listed on IDEAS
    4. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    5. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    6. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, Open Access Journal, vol. 3(4), pages 1-28, November.
    7. repec:eee:jbrese:v:76:y:2017:i:c:p:189-200 is not listed on IDEAS
    8. repec:spr:gjofsm:v:18:y:2017:i:3:d:10.1007_s40171-017-0159-3 is not listed on IDEAS
    9. López, Ana M., 2016. "El papel de la información económica como generador de conocimiento en el proceso de predicción: comparaciones empíricas del crecimiento del PIB regional /The Role of Economic Information as a Generat," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 34, pages 543-572, Agosto.
    10. repec:lrk:eeaart:35_2_5 is not listed on IDEAS
    11. repec:eee:intfor:v:33:y:2017:i:4:p:936-957 is not listed on IDEAS

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