Bias-corrected score decomposition for generalized quantiles

Abstract

Decompositions of the score of a forecast represent useful tools for assessing its performance. We consider local score decompositions permitting detailed forecast assessments across a spectrum of conditions of interest. We derive corrections to the bias of the decomposition components in the framework of point forecasts of quantile-type functionals, and illustrate their performance by simulation. Related bias corrections have thus far only been known for squared error criteria.