An Articial Counterfactuat Approach for Aggregate Data

We consider a new method to conduct counterfactual analysis with aggregate data when a ``treated'' unit suffers a shock or an intervention, such as a policy change. The proposed approach is based on the construction of an artificial counterfactual from a pool of ``untreated'' peers, and is inspired by different branches of the literature such as: the Synthetic Control method, the Global Vector Autoregressive models, the econometrics of structural breaks, and the counterfactual analysis based on macro-econometric and panel data models. We derive an asymptotic Gaussian estimator for the average effect of the intervention and present a collection of companion hypothesis tests. We also discuss finite sample properties and conduct a detailed Monte Carlo experiment