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Osband’s principle for identification functions

Author

Listed:
  • Timo Dimitriadis

    (Heidelberg University
    Heidelberg Institute for Theoretical Studies)

  • Tobias Fissler

    (Vienna University of Economics and Business (WU)
    ETH Zurich)

  • Johanna Ziegel

    (University of Bern)

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.

Suggested Citation

  • Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2024. "Osband’s principle for identification functions," Statistical Papers, Springer, vol. 65(2), pages 1125-1132, April.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-023-01428-x
    DOI: 10.1007/s00362-023-01428-x
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    References listed on IDEAS

    as
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    3. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
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    6. Alexander I. Jordan & Anja Mühlemann & Johanna F. Ziegel, 2022. "Characterizing the optimal solutions to the isotonic regression problem for identifiable functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 489-514, June.
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