Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/259715
Authors: 
Klößner, Stefan
Kaul, Ashok
Pfeifer, Gregor
Schieler, Manuel
Year of Publication: 
2018
Citation: 
[Journal:] Swiss Journal of Economics and Statistics [ISSN:] 2235-6282 [Volume:] 154 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2018 [Pages:] 1-11
Publisher: 
Springer, Heidelberg
Abstract: 
Recently, Abadie et al. (Am J Polit Sci 59:495-510, 2015) have expanded synthetic control methods by the so-called cross-validation technique. We find that their results are not being reproduced when alternative software packages are used or when the variables' ordering within the dataset is changed. We show that this failure stems from the cross-validation technique relying on non-uniquely defined predictor weights. While the amount of the resulting ambiguity is negligible for the main application of Abadie et al. (Am J Polit Sci 59:495-510, 2015), we find it to be substantial for several of their robustness analyses. Applying well-defined, standard synthetic control methods reveals that the authors' results are particularly driven by a specific control country, the USA.
Subjects: 
Synthetic control methods
Cross-validation
JEL: 
C23
C52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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