Synthetic Difference-in-Differences

• 成果类型: Article
• 署名作者: Arkhangelsky, Dmitry; Athey, Susan; Hirshberg, David A.; Imbens, Guido W.; Wager, Stefan
• 署名单位: Stanford University; National Bureau of Economic Research; Emory University; Stanford University; Stanford University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20190159
• 发表日期: 2021
• 页码: 4088-4118
• 关键词: panel-data models; regression; inference; selection; number

摘要

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference-in-differences and synthetic control methods. Relative to these methods we find, both theoretically and empirically, that this synthetic difference-in-differences estimator has desirable robustness properties, and that it performs well in settings where the conventional estimators are commonly used in practice. We study the asymptotic behavior of the estimator when the systematic part of the outcome model includes latent unit factors interacted with latent time factors, and we present conditions for consistency and asymptotic normality.