Data and Code for: Triplet Embeddings for Demand Estimation
Principal Investigator(s): View help for Principal Investigator(s) Lorenzo Magnolfi, University of Wisconsin-Madison; Jonathon McClure; Alan Sorensen
Version: View help for Version V1
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Project Description
Summary:
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Data and code for: Triplet Embeddings for Demand Estimation.
We propose a method to augment conventional demand estimation approaches with crowdsourced 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 (i) replace data on observed characteristics in mixed logit models, or (ii) 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.
We propose a method to augment conventional demand estimation approaches with crowdsourced 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 (i) replace data on observed characteristics in mixed logit models, or (ii) 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.
Scope of Project
Subject Terms:
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Embeddings;
demand estimation
JEL Classification:
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L10 Market Structure, Firm Strategy, and Market Performance: General
L10 Market Structure, Firm Strategy, and Market Performance: General
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