Assessing Point Forecast Accuracy by Stochastic Error Distance
Francis Diebold () and
Minchul Shin ()
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Minchul Shin: Department of Economics, University of Pennsylvania
PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania
We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 (\stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, showing that all such loss functions can be written as weighted SED's. The leading case is absolute-error loss, in which the SED weights are unity, establishing its primacy. Among other things, this suggests shifting attention away from conditional-mean forecasts and toward conditional-median forecasts.
Keywords: Forecast accuracy; forecast evaluation; absolute-error loss; quadratic loss; squared-error loss (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Working Paper: Assessing Point Forecast Accuracy by Stochastic Error Distance (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:14-038
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