My da|ra Login

Detailed view

metadata language: English

Replication data for: Cherry Picking with Synthetic Controls

Version
2
Resource Type
Dataset : other, program source code
Creator
  • Ferman, Bruno (Sao Paulo School of Economics - FGV)
  • Pinto, Cristine (Sao Paulo School of Economics - FGV)
  • Possebom, Vitor (Yale University)
Publication Date
2019-12-31
Description
  • Abstract

    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.
Availability
Download

Update Metadata: 2020-01-21 | Issue Number: 1 | Registration Date: 2020-01-21

Ferman, Bruno; Pinto, Cristine; Possebom, Vitor (2019): Replication data for: Cherry Picking with Synthetic Controls. Version: 2. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E117261V2