DAG-based local projections

Directed acyclic graphs (DAGs) provide a transparent framework for encoding causal structures and identifying causal effects. This paper demonstrates how DAGs help specify local projections (LPs) for estimating causal impulse responses. Examples illustrate how graphical rules can be used to select controls and instruments for identifying overall and path-specific effects. An empirical application to uncertainty shocks reveals substantial differences in the estimated responses of German industrial production across LP designs. The underlying DAGs help explain these differences and diagnose biases arising from violations of assumed causal structures. A DAG-based instrumental-variable LP reveals pronounced negative effects of U.S. uncertainty shocks.