Osband's Principle for Identification Functions

Abstract

Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, statistical estimation and dynamic modelling. For a possibly vector-valued functional of interest, we fully characterise the class of (strict) identification functions subject to mild regularity conditions.

References in corpus (1)

• Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability

Cited by in corpus (3)

• Regression Diagnostics meets Forecast Evaluation: Conditional Calibration, Reliability Diagrams, and Coefficient of Determination
• Elicitability and identifiability of tail risk measures
• Variable transformations in consistent loss functions