Econ Papers Daily / 期刊论文
基于文本与图像数据的需求估计
Demand Estimation with Text and Image Data
摘要
We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes or when consumers value hard-to-quantify attributes such as visual design. Using a choice experiment, we show this approach substantially outperforms standard attribute-based models at counterfactual predictions of second choices. We also apply it to 40 product categories offered on Amazon.com and consistently find that unstructured data are informative about substitution patterns.
中文摘要
我们提出了一种需求估计方法，利用非结构化数据来推断替代模式。通过使用预训练的深度学习模型，我们从产品图像和文本描述中提取嵌入，并将其纳入混合逻辑需求模型。这种方法使得即使研究者缺乏产品属性数据，或消费者重视难以量化的属性（如视觉设计）时，也能进行需求估计。通过选择实验，我们证明该方法在反事实预测第二选择方面显著优于基于属性的标准模型。我们还将此方法应用于Amazon.com上提供的40个产品类别，并一致发现非结构化数据对替代模式具有信息价值。