Data and Code for: Triplet Embeddings for Demand Estimation

Data and code for: Triplet Embeddings for Demand Estimation. <br><br>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.