  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 within each
regime. Based on four alternative monetary policy rules, we show that a monthly
six-variable system supports time variation in US monetary policy shock
identification. In the sample-dominating first regime, systematic monetary
policy follows a Taylor rule extended by the term spread, effectively curbing
inflation. In the second regime, occurring after 2000 and gaining more
persistence after the global financial and COVID crises, it is characterized by
a money-augmented Taylor rule. This regime's unconventional monetary policy
provides economic stimulus, features the liquidity effect, and is complemented
by a pure term spread shock. Absent the specific monetary policy of the second
regime, inflation would be over one percentage point higher on average after
2008.
