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Regression modelQuasi-experimental / causal inference

动态双重差分

动态双重差分(Dynamic DiD)将经典的双重差分(DiD)框架扩展到单位在不同时间采纳处理的场景。它不将所有变异折叠成单一的2x2比较,而是估计每个采纳队列在每个日历时期的群体-时间平均处理效应,然后将它们汇总成因果效应在事件时间上的可解释摘要。

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来源

  1. Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI: 10.1016/j.jeconom.2020.12.001
  2. Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI: 10.1016/j.jeconom.2020.09.006

如何引用本页

ScholarGate. (2026, June 3). Dynamic Difference-in-Differences Estimator. ScholarGate. https://scholargate.app/zh/causal-inference/dynamic-difference-in-differences

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被引用于

ScholarGateDynamic Difference-in-Differences (Dynamic Difference-in-Differences Estimator). 于 2026-06-15 检索自 https://scholargate.app/zh/causal-inference/dynamic-difference-in-differences · 数据集: https://doi.org/10.5281/zenodo.20539026