The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Synth_Runner Package: Utilities to Automate Synthetic Control Estimation Using Synth
Open accessAbstract
The synthetic control methodology (Abadie and Gardeazabal, 2003, American Economic Review 93: 113–132; Abadie, Diamond, and Hainmueller, 2010, Journal of the American Statistical Association 105: 493–505) allows for a data-driven approach to small-sample comparative studies. synth_runner automates the process of running multiple synthetic control estimations using synth. It conducts placebo estimates in space (estimations for the same treatment period but on all the control units). Inference ( p-values) is provided by comparing the estimated main effect with the distribution of placebo effects. It also allows several units to receive treatment, possibly at different time periods. It allows automatic generation of the outcome predictors and diagnostics by splitting the pretreatment into training and validation portions. Additionally, it provides diagnostics to assess fit and generates visualizations of results.
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The economic costs of conflict: a case study of the Basque Country
Abadie, A., & Gardeazabal, J. (2003) · American Economic Review · doi:10.1257/000282803321455188 · Cited by 4,517
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Metadata
- Authors
- Galiani, S., & Quistorff, B.
- Venue
- The Stata Journal: Promoting communications on statistics and Stata
- Publisher
- SAGE Publications
- Published
- 2017-12
- Volume
- 17
- Issue
- 4
- Pages
- 834-849
- ISSN
- 1536-867X (ISSN-L)
- 1536-8734
- OpenAlex
- W2788542558
- Cited by
- 263
- References
- 1
- FWCI
- 12.44
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Galiani, S., & Quistorff, B. (2017). The Synth_Runner Package: Utilities to Automate Synthetic Control Estimation Using Synth. The Stata Journal: Promoting communications on statistics and Stata. https://doi.org/10.1177/1536867x1801700404
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
