Point forecasting and forecast evaluation with generalized Huber loss

Huber loss, its asymmetric variants and their associated functionals (here named "Huber functionals") are studied in the context of point forecasting and forecast evaluation. The Huber functional of a distribution is the set of minimizers of the expected (asymmetric) Huber loss, is an intermediary between a quantile and corresponding expectile, and also arises in M-estimation. Each Huber functional is elicitable, generating the precise set of minimizers of an expected score, subject to weak regularity conditions on the class of probability distributions, and has a complete characterization of its consistent scoring functions. Such scoring functions admit a mixture representation as a weighted average of elementary scoring functions. Each elementary score can be interpreted as the relative economic loss of using a particular forecast for a class of investment decisions where profits and losses are capped. Finally, synthetic examples illustrate that in forecast evaluation Huber loss serves as robust scoring alternative to squared error for expectation point forecasts when some measurements of the realization are faulty.