Arbeitspapier

Synthetic Difference-in-Differences Estimation

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.

Sprache
Englisch

Erschienen in
Series: IZA Discussion Papers ; No. 15907

Klassifikation
Wirtschaft
Estimation: General
Econometric Software
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Model Evaluation, Validation, and Selection
Computational Techniques; Simulation Modeling
Thema
difference-in-differences
synthetic control
synthetic difference-in-differences
estimation
inference
visualization

Ereignis
Geistige Schöpfung
(wer)
Clarke, Damian
Pailañir, Daniel
Athey, Susan
Imbens, Guido W.
Ereignis
Veröffentlichung
(wer)
Institute of Labor Economics (IZA)
(wo)
Bonn
(wann)
2023

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Clarke, Damian
  • Pailañir, Daniel
  • Athey, Susan
  • Imbens, Guido W.
  • Institute of Labor Economics (IZA)

Entstanden

  • 2023

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