Conditional projection methods for large-scale Bayesian VARs
This work develops fast methods for forecasting and structural scenario analysis of large-scale Bayesian Vector Autoregressions (VARs). It uses a factor structure on errors to allow quick, order-invariant estimation across many variables. The method enables researchers to test how different assumptions about the source of economic shocks drastically change counterfactual predictions.