| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17247 |
| DP17247 Revisiting Event Study Designs: Robust and Efficient Estimation | |
| Kirill Borusyak; Xavier Jaravel; Jann Spiess | |
| 发表日期 | 2022-04-24 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Extensions include time-varying controls, triple-differences, and certain non-binary treatments. We show the practical relevance of these insights in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter — about half as large as benchmark estimates used to calibrate macroeconomic models — and predominantly occurs in the first month after the rebate. |
| 主题 | Labour Economics ; Macroeconomics and Growth |
| 关键词 | Difference-in-differences Event study Imputation estimator Panel data |
| URL | https://cepr.org/publications/dp17247 |
| 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/546254 |
| 推荐引用方式 GB/T 7714 | Kirill Borusyak,Xavier Jaravel,Jann Spiess. DP17247 Revisiting Event Study Designs: Robust and Efficient Estimation. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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