Joshua Foster, Fredrik Odegaard
arXiv 23 Jul 2025 · Econometrics
arXiv:2507.17564 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new demand estimation method using attention-based language models. An encoder-only language model is trained in a two-stage process to analyze the natural language descriptions of used cars from a large US-based online auction marketplace. The approach enables semi-nonparametrically estimation for the demand primitives of a structural model representing the private valuations and market size for each vehicle listing. In the first stage, the language model is fine-tuned to encode the target auction outcomes using the natural language vehicle descriptions. In the second stage, the trained language model's encodings are projected into the parameter space of the structural model. The model's capability to conduct counterfactual analyses within the trained market space is validated using a subsample of withheld auction data, which includes a set of unique "zero shot" instances.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and M… (2023) Towards general text embeddings with multi-stage contrastive learning, 2023 | 0.843 | 3 | 3 | 100% |
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| 4 | Adly Templeton (2024) Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet | 0.644 | 2 | 2 | 100% |
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| 9 | Xiao Liu (2023) Deep learning in marketing: a review and research agenda | 0.405 | 1 | 1 | 100% |
| 10 | Dinesh Puranam, Vrinda Kadiyali, and Vithala Narayan (2021) The impact of increase in minimum wages on consumer perceptions of service: A transformer model of online restaurant reviews | 0.405 | 1 | 1 | 100% |
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