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

• 成果类型: Article
• 署名作者: Masini, Ricardo; Medeiros, Marcelo C.
• 署名单位: Getulio Vargas Foundation; Pontificia Universidade Catolica do Rio de Janeiro; Princeton University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1964978
• 发表日期: 2021
• 页码: 1773-1788
• 关键词: error-correction; adaptive lasso; selection; cointegration; regression

摘要

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. for this article are available online.