Forecast Evaluation of Quantiles, Prediction Intervals, and other Set-Valued Functionals

TL;DR

This paper develops a theoretical framework for evaluating set-valued functionals like quantiles and prediction intervals, distinguishing between exhaustive and selective forecasts, and establishing their elicitability and identifiability properties.

Contribution

It introduces a novel framework for eliciting and identifying set-valued functionals, clarifies the distinction between exhaustive and selective forecasts, and provides new results on their elicitability.

Findings

Quantiles are selectively elicitable but not exhaustively elicitable.

Prediction intervals and Vorob'ev quantiles are both exhaustively elicitable and selectively identifiable.

The paper offers new scoring functions and Murphy diagrams for forecast evaluation.

Abstract

We introduce a theoretical framework of elicitability and identifiability of set-valued functionals, such as quantiles, prediction intervals, and systemic risk measures. A functional is elicitable if it is the unique minimiser of an expected scoring function, and identifiable if it is the unique zero of an expected identification function; both notions are essential for forecast ranking and validation, and $M$- and $Z$-estimation. Our framework distinguishes between exhaustive forecasts, being set-valued and aiming at correctly specifying the entire functional, and selective forecasts, content with solely specifying a single point in the correct functional. We establish a mutual exclusivity result: A set-valued functional can be either selectively elicitable or exhaustively elicitable or not elicitable at all. Notably, since quantiles are well known to be selectively elicitable, they fail to be exhaustively elicitable. We further show that the class of prediction intervals and Vorob'ev quantiles turn out to be exhaustively elicitable and selectively identifiable. In particular, we provide a mixture representation of elementary exhaustive scores, leading the way to Murphy diagrams. We give possibility and impossibility results for the shortest prediction interval and prediction intervals specified by an endpoint or a midpoint. We end with a comprehensive literature review on common practice in forecast evaluation of set-valued functionals.