| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w29879
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| 来源ID | Working Paper 29879
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| Trading-off Bias and Variance in Stratified Experiments and in Staggered Adoption Designs, Under a Boundedness Condition on the Magnitude of the Treatment Effect |
| Clément de Chaisemartin
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| 发表日期 | 2022-03-28
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| 出版年 | 2022
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| 语种 | 英语
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| 摘要 | I consider estimation of the average treatment effect (ATE), in a population composed of $G$ groups, when one has unbiased and uncorrelated estimators of each group's conditional average treatment effect (CATE). These conditions are met in stratified randomized experiments. I assume that the outcome is homoscedastic, and that each CATE is bounded in absolute value by B standard deviations of the outcome, for some known B. I derive, across all linear combinations of the CATEs' estimators, the estimator of the ATE with the lowest worst-case mean-squared error. This minimax-linear estimator assigns a weight equal to group g's share in the population to the most precisely estimated CATEs, and a weight proportional to one over the estimator's variance to the least precisely estimated CATEs. I also derive the minimax-linear estimator when the CATEs' estimators are positively correlated, a condition that may be met by differences-in-differences estimators in staggered adoption designs. |
| 主题 | Econometrics
; Estimation Methods
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| URL | https://www.nber.org/papers/w29879
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587551
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推荐引用方式
GB/T 7714 |
Clément de Chaisemartin. Trading-off Bias and Variance in Stratified Experiments and in Staggered Adoption Designs, Under a Boundedness Condition on the Magnitude of the Treatment Effect. 2022.
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文件名：
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w29879.pdf
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格式：
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Adobe PDF
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