Showing papers by "Francis X. Diebold published in 2014"
TL;DR: In this article, the authors propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0, and 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.
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.
2 citations
TL;DR: This work proposes and explores several related ways of reducing reliance of point forecast accuracy evaluation on expected loss, E(L(e)), where e is forecast error, and dispenses with the loss function entirely, instead using a "stochastic error divergence" (SED) accuracy measure based directly on the forecast-error c.d.f., F(e).
Abstract: We propose and explore several related ways of reducing reliance of point forecast accuracy evaluation on expected loss, E(L(e)), where e is forecast error. Our central approach dispenses with the loss function entirely, instead using a "stochastic error divergence" (SED) accuracy measure based directly on the forecast-error c.d.f., F(e). We explore several variations on the basic theme; interestingly, all point to the primacy of absolute-error loss and its generalizations.
1 citations