| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29378 |
| 来源ID | Working Paper 29378 |
| Economic Data Engineering | |
| Andrew Caplin | |
| 发表日期 | 2021-10-18 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Economic data engineering deliberately designs novel forms of data to solve fundamental identification problems associated with economic models of choice. I outline three diverse applications: to the economics of information; to life-cycle employment, earnings, and spending; and to public policy analysis. In all three cases one and the same fundamental identification problem is driving data innovation: that of separately identifying appropriately rich preferences and beliefs. In addition to presenting these conceptually linked examples, I provide a general overview of the engineering process, outline important next steps, and highlight larger opportunities. |
| 主题 | Other ; General, Teaching ; Econometrics ; Microeconomics ; Households and Firms ; Economics of Information |
| URL | https://www.nber.org/papers/w29378 |
| 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/587052 |
| 推荐引用方式 GB/T 7714 | Andrew Caplin. Economic Data Engineering. 2021. |
| 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 | ||
| w29378.pdf（560KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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