Triplet Embeddings for Demand Estimation

Lorenzo Magnolfi, Jonathon McClure and Alan Sorensen

American Economic Journal: Microeconomics, 2025, vol. 17, issue 1, 282-307

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.

JEL-codes: C45 C51 D11 D12 D21 L66 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1257/mic.20220248

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