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Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets

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  • Victor H. Aguiar
  • Nail Kashaev

Abstract

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a panel dataset on choices. We assume that either the latent choice sets are sparse or that the panel is sufficiently long. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested, or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer optimization to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to biased estimates of preferences due to significant latent heterogeneity in choice sets.

Suggested Citation

  • Victor H. Aguiar & Nail Kashaev, 2019. "Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets," Papers 1907.04853, arXiv.org, revised Jun 2021.
    • Handle: RePEc:arx:papers:1907.04853
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    References listed on IDEAS

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    Cited by:

    1. Kashaev, Nail & Aguiar, Victor H., 2022. "A random attention and utility model," Journal of Economic Theory, Elsevier, vol. 204(C).
    2. Ante Sterc, 2022. "Limited Consideration in the Investment Fund Choice," CERGE-EI Working Papers wp729, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

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