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Triplet Embeddings for Demand Estimation

Author

Listed:
  • Lorenzo Magnolfi
  • Jonathon McClure
  • Alan Sorensen

Abstract

We propose a method to augment conventional demand estimation approaches with crowd-sourced data on the product space. Our method obtains triplets data ("product A is closer to B than it is to C") from an online survey to compute an embedding—i.e., a low-dimensional representation of the latent product space. The embedding can either replace data on observed characteristics in mixed logit models, or provide pairwise product distances to discipline cross-elasticities in log-linear models. We illustrate both approaches by estimating demand for ready-to-eat cereals; the information contained in the embedding leads to more plausible substitution patterns and better fit.

Suggested Citation

  • Lorenzo Magnolfi & Jonathon McClure & Alan Sorensen, 2025. "Triplet Embeddings for Demand Estimation," American Economic Journal: Microeconomics, American Economic Association, vol. 17(1), pages 282-307, February.
    • Handle: RePEc:aea:aejmic:v:17:y:2025:i:1:p:282-307
    DOI: 10.1257/mic.20220248
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    References listed on IDEAS

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    1. Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
    2. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    3. Hausman, Jerry A. & Leonard, Gregory K., 2007. "Estimation of patent licensing value using a flexible demand specification," Journal of Econometrics, Elsevier, vol. 139(2), pages 242-258, August.
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    Cited by:

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    2. Afonso Rodrigues, 2025. "Consumer Choice Over Shopping Baskets," Papers 2511.11846, arXiv.org, revised May 2026.
    3. Kevin Zielnicki & Guy Aridor & Aur'elien Bibaut & Allen Tran & Winston Chou & Nathan Kallus, 2025. "The Value of Personalized Recommendations: Evidence from Netflix," Papers 2511.07280, arXiv.org, revised Jun 2026.
    4. Timothy Christensen & Giovanni Compiani, 2026. "From Unstructured Data to Demand Counterfactuals: Theory and Practice," Papers 2601.05374, arXiv.org.

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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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