  A comprehensive methodology for inference in vector autoregressions (VARs)
using sign and other structural restrictions is developed. The reduced-form VAR
disturbances are driven by a few common factors and structural identification
restrictions can be incorporated in their loadings in the form of parametric
restrictions. A Gibbs sampler is derived that allows for reduced-form
parameters and structural restrictions to be sampled efficiently in one step. A
key benefit of the proposed approach is that it allows for treating parameter
estimation and structural inference as a joint problem. An additional benefit
is that the methodology can scale to large VARs with multiple shocks, and it
can be extended to accommodate non-linearities, asymmetries, and numerous other
interesting empirical features. The excellent properties of the new algorithm
for inference are explored using synthetic data experiments, and by revisiting
the role of financial factors in economic fluctuations using identification
based on sign restrictions.
