Synthetic Difference-in-Differences Estimation

Damian Clarke, Daniel Pailañir (), Susan Athey and Guido W. Imbens ()
Additional contact information
Daniel Pailañir: University of Chile
Guido W. Imbens: Stanford University

No 15907, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-in-differences can be used in a wide class of circumstances where treatment effects on some particular policy or event are desired, and repeated observations on treated and untreated units are available over time. We lay out the theory underlying SDID, both when there is a single treatment adoption date and when adoption is staggered over time, and discuss estimation and inference in each of these cases. We introduce the sdid command which implements these methods in Stata, and provide a number of examples of use, discussing estimation, inference, and visualization of results.

Keywords: synthetic difference-in-differences; synthetic control; difference-in-differences; estimation; inference; visualization (search for similar items in EconPapers)
JEL-codes: C13 C23 C52 C63 C87 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2023-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Published - published in: Stata Journal, 2024, 24 (4), 557-598

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Related works:
Working Paper: Synthetic Difference In Differences Estimation (2023)
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