Time-series estimation of the effects of natural experiments

This paper builds on the labor econometrics and classical treatment effects literatures to provide a framework supporting causal concepts and methods for estimating effects of natural experiments operating over time in an explicitly dynamic time-series context. We examine conditions for the construction of covariates instrumental in identifying effects of interest that lead to new tests for unconfoundedness, a key condition for the identification of causal effects that we link to the concept of Granger non-causality. Our new tests for unconfoundedness are useful in both cross-section and dynamic time-series settings.