Before revision: In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects. We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series literature.
After revision: In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks. We start by arguing that commonly used estimation procedures tend to ignore the crucial time-series literature.