Synthetic Difference-in-Differences
Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager
American Economic Review · 2021 · Volume 111 · Issue 12
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.
Keywords
Single Equation Models, Single Variables: Panel Data Models, Spatio-temporal Models, Business Taxes and Subsidies including sales and value-added (VAT), State and Local Taxation, Subsidies, and Revenue, Health: Government Policy, Regulation, Public Health, Food, Beverages, Cosmetics, Tobacco, Wine and Spirits
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