用AI分析玩具车销量，提升预测和因果推断精度。
Adventures in Demand Analysis Using AI
- 用Transformer模型融合文本、图像等多模态数据生成产品嵌入向量。
- 嵌入向量使销量与价格预测准确率显著提升，价格弹性估计更可信。
- 发现产品特性差异导致价格弹性存在明显异质性，适合做需求分析研究者参考。
本文通过整合人工智能生成的多模态产品表征，推进了实证需求分析。基于Amazon.com上玩具车的详细数据集，结合文本描述、图像和表格协变量，使用基于Transformer的嵌入模型表示每个产品。这些嵌入捕捉了质量、品牌和视觉特征等细微属性，传统方法难以有效概括。同时，我们对嵌入进行微调以支持因果推断任务。结果表明，所获嵌入显著提升了销量排名和价格的预测准确性，并使价格弹性的因果估计更加可信。值得注意的是，我们发现了由产品特定特征驱动的强烈价格弹性异质性。研究结果表明，基于AI的产品表征能够丰富并现代化实证需求分析，其洞察对更广泛的因果推断应用也具价值。
原文摘要 · Abstract (English)
This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly.
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