Synthetic Difference-in-Differences
- (pp. 4088-4118)
Abstract
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.Citation
Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. 2021. "Synthetic Difference-in-Differences." American Economic Review, 111 (12): 4088-4118. DOI: 10.1257/aer.20190159Additional Materials
JEL Classification
- C23 Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- H25 Business Taxes and Subsidies including sales and value-added (VAT)
- H71 State and Local Taxation, Subsidies, and Revenue
- I18 Health: Government Policy; Regulation; Public Health
- L66 Food; Beverages; Cosmetics; Tobacco; Wine and Spirits