I extend the Bayesian Factor-Augmented Vector Autoregressive model (FAVAR) to incorporate an identification scheme based on an exogenous variable approach. A Gibbs sampling algorithm is provided to estimate the posterior distributions of the models parameters. I estimate the effects of a...

Traditional ways of analyzing the effects of monetary policy shocks via structural vector autoregressions require the use of unrealistic identifying assumptions: they either do not allow for a response of output and prices on impact of the shock, or they exclude contemporaneous values of these...

We propose a new Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects alternative exclusion restrictions over time and, as a condition for the search, allows to verify identification through heteroskedasticity...