Machine learning the macroeconomic effects of financial shocks

We propose a method to learn the nonlinear impulse responses to structural shocks using neural networks, and apply it to uncover the effects of US financial shocks. The results reveal substantial asymmetries with respect to the sign of the shock. Adverse financial shocks have powerful effects on the US economy, while benign shocks trigger much smaller reactions. Instead, with respect to the size of the shocks, we find no discernible asymmetries. • Bayesian Neural Networks to uncover nonlinear effects of financial shocks. • Simulations confirm BNNs accurately capture sign and size asymmetries. • Financial shocks impact inflation, output, and employment asymmetrically. • Adverse shocks trigger stronger reactions than benign ones.