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Generic Conditions for Forecast Dominance

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  • Fabian Krüger
  • Johanna F. Ziegel

Abstract

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, for example, from model misspecification or parameter estimation error. We illustrate the empirical relevance of our results via data examples from finance and economics.

Suggested Citation

  • Fabian Krüger & Johanna F. Ziegel, 2021. "Generic Conditions for Forecast Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 972-983, October.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:4:p:972-983
    DOI: 10.1080/07350015.2020.1741376
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    Cited by:

    1. Fissler, Tobias & Pesenti, Silvana M., 2023. "Sensitivity measures based on scoring functions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1408-1423.
    2. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    3. Fissler, Tobias & Merz, Michael & Wüthrich, Mario V., 2023. "Deep quantile and deep composite triplet regression," Insurance: Mathematics and Economics, Elsevier, vol. 109(C), pages 94-112.
    4. Denuit, Michel & Trufin, Julien, 2022. "Tweedie dominance for autocalibrated predictors and Laplace transform order," LIDAM Discussion Papers ISBA 2022040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Denuit, Michel & Trufin, Julien, 2022. "Autocalibration by balance correction in nonlife insurance pricing," LIDAM Discussion Papers ISBA 2022041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Mario V. Wuthrich & Johanna Ziegel, 2023. "Isotonic Recalibration under a Low Signal-to-Noise Ratio," Papers 2301.02692, arXiv.org.
    7. Arthur Charpentier, 2022. "Quantifying fairness and discrimination in predictive models," Papers 2212.09868, arXiv.org.
    8. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    9. Denuit, Michel & Trufin, Julien, 2022. "Model selection with Pearson’s correlation, concentration and Lorenz curves under autocalibration," LIDAM Discussion Papers ISBA 2022033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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