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.

需求估计的三元组嵌入：数据与代码<br><br>我们提出一种方法，利用产品空间的众包数据增强传统的需求估计方法。该方法通过在线调查获取三元组数据（如‘产品A与B的相似度高于A与C’），以计算嵌入（embedding）——即潜在产品空间的低维表示。该嵌入可用于两种场景：（i）替代混合Logit模型中的观测特征数据；（ii）提供产品间两两距离，以约束对数线性模型中的交叉弹性。我们通过估计即食麦片的需求来演示这两种方法；嵌入所包含的信息可产生更合理的替代模式和更优的拟合效果。