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...

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...

Obtaining reliable estimates of the volatility of interest rates and exchange rates is a necessary condition to evaluate issues related to monetary independence and fear of floating. In this paper we use methods which explicitly account for structural breaks in the volatility dynamics in order...