Averaging quantiles, variance shrinkage, and overconfidence

Roger Cooke

Futures & Foresight Science, 2023, vol. 5, issue 1

Abstract: Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically. Quantile averaging is equivalent to taking the harmonic mean of densities evaluated at quantile points. A variance shrinkage law is established between equal and harmonic weighting. Data from 49 post‐2006 studies are extended to include harmonic weighting in addition to equal and performance‐based weighting. It emerges that harmonic weighting has the highest average information and degraded statistical accuracy. The hypothesis that the quantile average is statistically accurate would be rejected at the 5% level in 28 studies and at the 0.1% level in 15 studies. For performance weighting, these numbers are 3 and 1, for equal weighting 2 and 1.

Date: 2023
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https://doi.org/10.1002/ffo2.139

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Persistent link: https://EconPapers.repec.org/RePEc:wly:fufsci:v:5:y:2023:i:1:n:e139

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