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A survey of preference estimation with unobserved choice set heterogeneity

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  • Crawford, Gregory S.
  • Griffith, Rachel
  • Iaria, Alessandro

Abstract

We provide an introduction to the estimation of discrete choice models when choice sets are heterogeneous and unobserved to the econometrician. We survey the two most popular approaches: “integrating over” and “differencing out” unobserved choice sets. Inspired by Chamberlain (1980)’s original idea of constructing sufficient statistics from observed choices, we introduce the term “sufficient set” to refer to any combination of observed choices that lies within the true but unobserved choice set. The concept of sufficient set helps to unify notation and organize our thinking, to map econometric assumptions onto economic models, and to implement both methods in practice.

Suggested Citation

  • Crawford, Gregory S. & Griffith, Rachel & Iaria, Alessandro, 2021. "A survey of preference estimation with unobserved choice set heterogeneity," Journal of Econometrics, Elsevier, vol. 222(1), pages 4-43.
    • Handle: RePEc:eee:econom:v:222:y:2021:i:1:p:4-43
    DOI: 10.1016/j.jeconom.2020.07.024
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    Cited by:

    1. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    2. Nail Kashaev & Natalia Lazzati & Ruli Xiao, 2023. "Peer Effects in Consideration and Preferences," Papers 2310.12272, arXiv.org, revised Jan 2024.
    3. Victor Aguirregabiria & Alessandro Iaria & Senay Sokullu, 2023. "Identification and Estimation of Demand Models with Endogenous Product Entry and Exit," Working Papers tecipa-755, University of Toronto, Department of Economics.
    4. Siddhartha Chib & Kenichi Shimizu, 2023. "Scalable Estimation of Multinomial Response Models with Uncertain Consideration Sets," Papers 2308.12470, arXiv.org, revised Oct 2023.
    5. 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.
    6. Cristina Gualdani & Shruti Sinha, 2023. "Identification in Discrete Choice Models with Imperfect Information," Working Papers 949, Queen Mary University of London, School of Economics and Finance.
    7. Takeshi Fukasawa, 2022. "The Biases in Applying Static Demand Models under Dynamic Demand," Discussion Paper Series DP2022-18, Research Institute for Economics & Business Administration, Kobe University, revised Jul 2022.
    8. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
    9. C. Angelo Guevara, 2022. "A Note on "A survey of preference estimation with unobserved choice set heterogeneity" by Gregory S. Crawford, Rachel Griffith, and Alessandro Iaria," Papers 2205.00852, arXiv.org.

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    More about this item

    Keywords

    Discrete choice models; Panel data; Unobserved choice sets; Unobserved heterogeneity; Sufficient sets;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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