| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27704 |
| 来源ID | Working Paper 27704 |
| Nonparametric Identification of Differentiated Products Demand Using Micro Data | |
| Steven T. Berry; Philip A. Haile | |
| 发表日期 | 2020-08-17 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | A recent literature considers the identification of heterogeneous demand and supply models via "quasi-experimental'' variation, as from instrumental variables. In this paper we establish nonparametric identification of differentiated products demand when one has "micro data'' linking characteristics of individual consumers to their choices. Micro data provide a panel structure allowing one to exploit variation across consumers within each market, where latent demand shocks are fixed. This facilitates richer demand specifications while substantially softening the reliance on instrumental variables, reducing both the number and types of instruments required. Our results require neither the structure of a "special regressor'' nor a "full support'' assumption on consumer-level observables. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Households and Firms ; Industrial Organization |
| URL | https://www.nber.org/papers/w27704 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585376 |
| 推荐引用方式 GB/T 7714 | Steven T. Berry,Philip A. Haile. Nonparametric Identification of Differentiated Products Demand Using Micro Data. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w27704.pdf（358KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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