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
  R 07/28/2021 07:09:PM
  data 07/28/2021 07:09:PM
  inst 07/28/2021 07:09:PM
  man 07/28/2021 07:09:PM
  tests 07/28/2021 07:09:PM
  vignettes 07/28/2021 07:09:PM
.Rbuildignore text/plain 161 bytes 07/28/2021 03:09:PM
.gitignore text/plain 176 bytes 07/28/2021 03:09:PM
DESCRIPTION text/plain 809 bytes 07/28/2021 03:09:PM
DEVELOPING.md text/x-web-markdown 1.9 KB 07/28/2021 03:09:PM

Citation: 

Arkhangelsky, Dmitry, Athey, Susan, A. Hirshberg, David, Imbens, Guido W., and Wager, Stefan. Data and code for:  Synthetic Difference in Differences: synthdid sdid paper. 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/E146381V1-155621

To view the citation for the overall project, see http://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


Related Publications

Download this project

Published Versions

V1 [2021-11-19]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.