A Comparison of Dynamic Causal Inference Methods

The estimation of dynamic causal effects is one of the crucial challenges of econometrics. In the macroeconomic literature, dynamic causal effects are conceived as the effect, over time, of an intervention that propagates through the economy. This is usually modeled via impulse response analysis on vector auto-regressive (VAR) models. In this study, we compare the impulse response estimator of a VAR, local projection, and a Bayesian network VAR (BNVAR) model. Our results show that the BNVAR impulse response estimators are generally more efficient than local projection and VAR estimators