We propose a straightforward algorithm to estimate large Bayesian time-varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time-variation in...

This paper develops a multivariate regime switching monetary policy model for the US economy. To exploit a large dataset we use a factor-augmented VAR with discrete regime shifts, capturing distinct business cycle phases. The transition probabilities are modelled as time-varying, depending on a...

This paper develops a global vector autoregressive (GVAR) model with time-varying parameters and stochastic volatility to analyze whether international spillovers of US monetary policy have changed over time. The proposed model allows assessing whether coefficients evolve gradually over time or...

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model...

We analyze the interaction between monetary policy in the US and the global economy, using a global vector autoregressive model with time-varying parameters and stochastic volatility (TVP-SV-GVAR). We find that a contractionary US monetary policy shock leads to a persistent fall in international...