Data and code for: Synthetic Difference in Differences

Principal Investigator(s):  View help for Principal Investigator(s) Dmitry Arkhangelsky, CEMFI; Susan Athey, Stanford University; David A. Hirshberg, Emory University; Guido W. Imbens, Stanford University; Stefan Wager, Stanford University

Version:  View help for Version V1

Name File Type Size Last Modified
  synthdid-sdid-paper 07/28/2021 07:09:PM

Project Citation: 

Arkhangelsky, Dmitry, Athey, Susan, A. Hirshberg, David, Imbens, Guido W., and Wager, Stefan. Data and code for:  Synthetic Difference in Differences. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-19. https://doi.org/10.3886/E146381V1

Project Description

Summary:  View help for Summary 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.

This folder provides a copy of https://github.com/synth-inference/synthdid/tree/sdid-paper that replicates all the results in the paper.

Scope of Project

JEL Classification:  View help for JEL Classification
      C01 Econometrics
      C23 Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models


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Published Versions

V1 [2021-11-19]

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