| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w21842 |
| 来源ID | Working Paper 21842 |
| Using Split Samples to Improve Inference about Causal Effects | |
| Marcel Fafchamps; Julien Labonne | |
| 发表日期 | 2016-01-11 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. The method – that can be combined with pre-analysis plans – reduces the risk that relevant hypotheses are left untested. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w21842 |
| 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/579516 |
| 推荐引用方式 GB/T 7714 | Marcel Fafchamps,Julien Labonne. Using Split Samples to Improve Inference about Causal Effects. 2016. |
| 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 | ||
| w21842.pdf（329KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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