Replication data for: Cherry Picking with Synthetic Controls
Principal Investigator(s): View help for Principal Investigator(s) Bruno Ferman, Sao Paulo School of Economics - FGV; Cristine Pinto, Sao Paulo School of Economics - FGV; Vitor Possebom, Yale University
Version: View help for Version V2
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application/zip | 1.1 GB | 01/16/2020 04:45:AM |
Project Citation:
Ferman, Bruno, Pinto, Cristine, and Possebom, Vitor. Replication data for: Cherry Picking with Synthetic Controls. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-01-21. https://doi.org/10.3886/E117261V2
Project Description
Summary:
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We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification-searching opportunities. We provide theoretical results showing that specification-searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specification searching when the number of pre-treatment periods is in line with common SC applications, and when alternative specifications commonly used in SC applications are also considered. This suggests that such lack of guidance generates a substantial level of discretion in the choice of the comparison units in SC applications, undermining one of the advantages of the method. We provide recommendations to limit the possibilities for specification searching in the SC method. Finally, we analyze the possibilities for specification searching and our recommendations in a series of empirical applications.
Scope of Project
Data Type(s):
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other;
program source code
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