Assessing Point Forecast Accuracy by Stochastic Error Distance

Francis Diebold and Minchul Shin

No 22516, NBER Working Papers from National Bureau of Economic Research, Inc

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, and we show that all such loss functions can be written as weighted SED's. The leading case is absolute-error loss. Among other things, this suggests shifting attention away from conditional-mean forecasts and toward conditional-median forecasts.

JEL-codes: C52 C53 (search for similar items in EconPapers)
Date: 2016-08
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
Note: AP EFG
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Published as Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, vol 36(6-9), pages 588-598.

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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 (2014)
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