Abstract
Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of “untre ated” peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual analysis for high-dimensional, nonstationary data with either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. We propose a resampling procedure to test intervention effects that does not rely on postintervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application. Supplementary materials for this article are available online.
Original language | English (US) |
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Pages (from-to) | 1773-1788 |
Number of pages | 16 |
Journal | Journal of the American Statistical Association |
Volume | 116 |
Issue number | 536 |
DOIs | |
State | Published - 2021 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- Cointegration
- Comparative studies
- Intervention
- Policy evaluation
- Resampling
- Synthetic control
- panel data