时间在你身边:在差异差异研究中汇总数据
Summer Rak1, Laura A Hatfield2, Carrie E Fry3
1RTI International, Research Triangle Park, North Carolina, USA.
Health services research
|May 27, 2025
概括
在许多情况下,时间聚合对差异差异 (DID) 估计器的影响最小. 然而,对于具有分期定时或不平衡数据的动态效果,更细的时间尺度会增加功率,但降低精度.
科学领域:
- 计量经济学 计量经济学 计量经济学
- 因果推理因果推理
- 统计建模 统计建模
背景情况:
- 差异差异 (DID) 估计器被广泛用于因果推理.
- 数据聚合的时间尺度的选择可以影响DID估计器的性能.
- 了解这种影响对于准确的政策和治疗效果评估至关重要.
研究的目的:
- 将DID估计器的性能与不同时间尺度 (每月,每季度,每年) 汇总的数据进行比较.
- 评估时间聚合如何影响对待的静态和动态平均治疗效应 (ATT) 的估计.
主要方法:
- 使用参数和重新采样模型进行模拟,使用不同的面板平衡和处理时间.
- 使用线性回归和卡拉威和圣安娜 (2021) 估计器估计静态和动态ATT.
- 基于偏差,标准错误,RMSE,功率和I型错误的月度,季度和年度汇总数据的比较.
- 一个用警察再培训数据来说明现实世界的影响的案例研究.
主要成果:
- 时间聚合的影响因性能指标,估计方法和数据结构而异.
- 动态治疗效应,不平衡的面板数据和重新采样模拟显示出对时间聚合的更高灵敏度.
- 在重新采样模拟中,采用分阶时间,更粗的聚合得到了青.
- 对警察培训数据的重新分析表明,对时间聚合的敏感性.
结论:
- 时间聚合通常对DID估计器的影响微不足道.
- 估计动态效应与分阶段时间和不平衡的数据呈现精确功率的权衡,有利于更精细的聚合功率,但降低精度.
- 单个参考时间点估计器在更细的时间尺度上更容易受到噪声的影响.
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