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Universal Inference for Incomplete Discrete Choice Models

Author

Listed:
  • Hiroaki Kaido
  • Yi Zhang

Abstract

A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components.

Suggested Citation

  • Hiroaki Kaido & Yi Zhang, 2025. "Universal Inference for Incomplete Discrete Choice Models," Papers 2501.17973, arXiv.org.
    • Handle: RePEc:arx:papers:2501.17973
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    References listed on IDEAS

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    8. Elie Tamer, 2003. "Incomplete Simultaneous Discrete Response Model with Multiple Equilibria," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(1), pages 147-165.
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