American Economic Journal:
Microeconomics
ISSN 1945-7669 (Print) | ISSN 1945-7685 (Online)
Triplet Embeddings for Demand Estimation
American Economic Journal: Microeconomics
vol. 17,
no. 1, February 2025
(pp. 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.Citation
Magnolfi, Lorenzo, Jonathon McClure, and Alan Sorensen. 2025. "Triplet Embeddings for Demand Estimation." American Economic Journal: Microeconomics, 17 (1): 282–307. DOI: 10.1257/mic.20220248Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- C51 Model Construction and Estimation
- D11 Consumer Economics: Theory
- D12 Consumer Economics: Empirical Analysis
- D21 Firm Behavior: Theory
- L66 Food; Beverages; Cosmetics; Tobacco; Wine and Spirits
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