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Testing for more positive expectation dependence with application to model comparison

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  • Denuit, Michel
  • Trufin, Julien
  • Verdebout, Thomas

Abstract

Modern data science tools are effective to produce predictions that strongly correlate with responses. Model comparison can therefore be based on the strength of dependence between responses and their predictions. Positive expectation dependence turns out to be attractive in that respect. The present paper proposes an effective testing procedure for this dependence concept and applies it to compare two models. A simulation study is performed to evaluate the performances of the proposed testing procedure. Empirical illustrations using insurance loss data demonstrate the relevance of the approach for model selection in supervised learning. The most positively expectation dependent predictor can then be autocalibrated to obtain its balance-corrected version that appears to be optimal with respect to Bregman, or forecast dominance.

Suggested Citation

  • Denuit, Michel & Trufin, Julien & Verdebout, Thomas, 2021. "Testing for more positive expectation dependence with application to model comparison," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 163-172.
    • Handle: RePEc:eee:insuma:v:101:y:2021:i:pb:p:163-172
    DOI: 10.1016/j.insmatheco.2021.07.008
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    References listed on IDEAS

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    More about this item

    Keywords

    Expectation dependence; Concentration curve; Lorenz curve; Autocalibration; Convex order; Balance correction;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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