| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16933 |
| DP16933 Flexible Demand Estimation with Search Data | |
| Tomomichi Amano; Andrew Rhodes; Stephan Seiler | |
| 发表日期 | 2022-05-25 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Traditional methods for estimating demand are not always well-suited to online markets, where individual products are sold infrequently, unobserved factors such as webpage layout drive substitution, and often only a limited set of product characteristics is observed. We propose a demand model where browsing data—which is abundant in many online settings— is used to infer individual consumers' consideration sets. In our model, the underlying variables which drive consideration can be correlated arbitrarily across products. We estimate the model through a constraint maximization approach, based on the insight that these correlations should rationalize the product-pair co-search frequencies that are observed in the data. In turn, these correlations make it possible to estimate more flexible substitution patterns. We apply the model to data from an online retailer, recover the elasticity matrix, and solve for optimal prices. |
| 主题 | Industrial Organization |
| 关键词 | Demand estimation Consideration sets Consumer search |
| URL | https://cepr.org/publications/dp16933-0 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/546362 |
| 推荐引用方式 GB/T 7714 | Tomomichi Amano,Andrew Rhodes,Stephan Seiler. DP16933 Flexible Demand Estimation with Search Data. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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