Combining Forecasts: Performance and Coherence

Mary Thomson, Andrew C. Pollock, Dilek Onkal, Mustafa Sinan Gonul

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
14 Downloads (Pure)


There is general agreement in many forecasting contexts that combining individual predictions leads to better final forecasts. However, the relative error reduction in a combined forecast depends upon the extent to which the component forecasts contain unique/independent information. Unfortunately, obtaining independent predictions is difficult in many situations, as these forecasts may be based on similar statistical models and/or overlapping information. The current study addresses this problem by incorporating a measure of coherence into an analytic evaluation framework so that the degree of independence between sets of forecasts can be identified easily. The framework also decomposes the performance and coherence measures in order to illustrate the underlying aspects that are responsible for error reduction. The framework is demonstrated using UK retail prices index inflation forecasts for the period 1998–2014, and implications for forecast users are discussed.

Original languageEnglish
Pages (from-to)474-484
Number of pages11
JournalInternational Journal of Forecasting
Issue number2
Early online date28 Dec 2018
Publication statusPublished - 1 Apr 2019


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