Proxy VAR models in a data-rich environment

Structural VAR models require two ingredients: (i) Informational sufficiency, and (ii) a valid identification strategy. These conditions are unlikely to be met by small-scale recursively identified VAR models. I propose a Bayesian Proxy Factor-Augmented VAR (BP-FAVAR) to combine a large information set with an identification scheme based on an external instrument. In an application to monetary policy shocks I find that augmenting a standard small-scale Proxy VAR by factors from a large set of financial variables changes the model dynamics and delivers price responses which are more in line with economic theory. A second application shows that an exogenous increase in uncertainty affects disaggregated investment series more negatively than consumption series.