Working Paper: NBER ID: w22516
Authors: Francis X. Diebold; Minchul Shin
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.
Keywords: No keywords provided
JEL Codes: C52; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| stochastic error distance (SED) (C20) | expected absolute error loss (C51) |
| stochastic error distance (SED) (C20) | accuracy of forecasts (C53) |
| choice of ranking method (SED vs. traditional loss) (C52) | perceived accuracy of forecasts (C53) |
| weighted version of SED (C20) | expected lin-lin loss (C51) |
| divergence of SED rankings (A14) | traditional rankings (like mean squared error) (C52) |