针对重复横截面数据的合成双重差分法
Synthetic Difference in Differences for Repeated Cross-Sectional Data
AI总结：
本文将合成双重差分法扩展到重复横截面数据，通过按组聚合数据并引入考虑各横截面观测数量差异的第三种权重，模拟显示该方法能有效提升估计性能。
摘要：
合成双重差分方法提供了一种利用潜在因子模型估计因果效应的有效方法。然而，该方法依赖于面板数据的使用。本文提出了合成双重差分方法针对重复横截面数据的一种改进形式。处理被视为发生在组层面，因此可以通过按组聚合数据，在这些聚合数据上计算两种类型的合成双重差分权重。随后，本文开发并计算了第三种权重，该权重考虑了每个横截面中观测数量的不同。模拟结果表明，在重复横截面数据上使用第三种权重时，合成双重差分估计量的性能得到了改善。
英文摘要：
The synthetic difference-in-differences method provides an efficient method to estimate a causal effect with a latent factor model. However, it relies on the use of panel data. This paper presents an adaptation of the synthetic difference-in-differences method for repeated cross-sectional data. The treatment is considered to be at the group level so that it is possible to aggregate data by group to compute the two types of synthetic difference-in-differences weights on these aggregated data. Then, I develop and compute a third type of weight that accounts for the different number of observations in each cross-section. Simulation results show that the performance of the synthetic difference-in-differences estimator is improved when using the third type of weights on repeated cross-sectional data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
