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Approximating Choice Data by Discrete Choice Models

Author

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  • Haoge Chang
  • Yusuke Narita
  • Kota Saito
Abstract
We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric random utility models arbitrarily well across choice sets. The condition turns out to be the affine-independence of the set of characteristic vectors. When the condition fails, resulting in some random utility models that cannot be closely approximated, we identify preferences and substitution patterns that are challenging to approximate accurately. We also propose algorithms to quantify the magnitude of approximation errors.

Suggested Citation

  • Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2205.01882
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    References listed on IDEAS

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    Cited by:

    1. Jean-Paul Doignon & Kota Saito, 2022. "Adjacencies on random ordering polytopes and flow polytopes," Papers 2207.06925, arXiv.org.

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