# Generic Conditions for Forecast Dominance

arXiv: https://arxiv.org/abs/1805.09902 · HTML: https://mlsanity.com/paper/1805.09902/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2018.

## Built on by (most cited first, 13 of 13)

- [Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning](https://mlsanity.com/paper/2103.03635/) (2021)
- [Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice](https://mlsanity.com/paper/2202.12780/) (2022)
- [Calibrating sufficiently](https://mlsanity.com/paper/2105.07283/) (2021)
- [Isotonic Recalibration under a Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2301.02692/) (2023)
- [Evaluating Probabilistic Classifiers: The Triptych](https://mlsanity.com/paper/2301.10803/) (2023)
- [A Fair Pricing Model via Adversarial Learning](https://mlsanity.com/paper/2202.12008/) (2022)
- [Model selection with Gini indices under auto-calibration](https://mlsanity.com/paper/2207.14372/) (2022)
- [Hierarchies of Calibration: Classification meets Regression](https://mlsanity.com/paper/2606.03245/) (2026)
- [Geospatial Disparities: A Case Study on Real Estate Prices in Paris](https://mlsanity.com/paper/2401.16197/) (2024)
- [Calibration Bands for Mean Estimates within the Exponential Dispersion Family](https://mlsanity.com/paper/2503.18896/) (2025)
- [Assessing model calibration with boosting trees](https://mlsanity.com/paper/2606.08084/) (2026)
- [CAIRO: Decoupling Order from Scale in Regression](https://mlsanity.com/paper/2602.14440/) (2026)
- [Inductive Venn-Abers and related regressors](https://mlsanity.com/paper/2605.06646/) (2026)

## Abstract (arXiv)

Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical conditions under which one forecast dominates another. To address this question, we derive a new characterization of dominance among forecasts of the mean functional. We present various scenarios under which dominance occurs. Unlike existing results, our results allow for the case that the forecasts' underlying information sets are not nested, and allow for uncalibrated forecasts that suffer, e.g., from model misspecification or parameter estimation error. We illustrate the empirical relevance of our results via data examples from finance and economics.