| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12804 |
| DP12804 Incentive Compatible Estimators | |
| Kfir Eliaz; Ran Spiegler | |
| 发表日期 | 2018-03-22 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We study a model in which a "statistician" takes an action on behalf of an agent, based on a random sample involving other people. The statistician follows a penalized regression procedure: the action that he takes is the dependent variable's estimated value given the agent's disclosed personal characteristics. We ask the following question: Is truth-telling an optimal disclosure strategy for the agent, given the statistician's procedure? We discuss possible implications of our exercise for the growing reliance on "machine learning" methods that involve explicit variable selection. |
| 主题 | Industrial Organization |
| URL | https://cepr.org/publications/dp12804 |
| 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/541613 |
| 推荐引用方式 GB/T 7714 | Kfir Eliaz,Ran Spiegler. DP12804 Incentive Compatible Estimators. 2018. |
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