Assessing Point Forecast Accuracy by Stochastic Error Distance

Francis Diebold and Minchul Shin

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: 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)
Pages: 18 pages
Date: 2014-11-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Assessing point forecast accuracy by stochastic error distance (2017)
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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