Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity

Ricardo Masini and Marcelo Medeiros ()

Journal of the American Statistical Association, 2021, vol. 116, issue 536, 1773-1788

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.

Date: 2021
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Citations: View citations in EconPapers (13)

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DOI: 10.1080/01621459.2021.1964978

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