Scoring rules and survey density forecasts
Gianna Boero (),
Jeremy Smith and
Kenneth Wallis ()
International Journal of Forecasting, 2011, vol. 27, issue 2, 379-393
This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.
Keywords: Density forecast evaluation; Brier (quadratic probability) score; Epstein (ranked probability) score; Logarithmic score; Bank of England Survey of External Forecasters; Missing data; Forecast comparison (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y:2011:i:2:p:379-393
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