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 estimate a multivariate unobserved components-stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US Dollar. The empirical model is based on the assumption that both countries' monetary policy strategies may be well described by Taylor rules with a...

We estimate a multivariate unobserved components-stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US dollar. The empirical model is based on the assumption that both countries' monetary policy strategies may be well described by Taylor rules with a...

We propose a dynamic factor model with time-varying parameters and stochastic volatility to analyze the relationship between global factors and country-specific capital flow dynamics. Studying a global sample of 43 countries from 1994 until 2015, we show that global co-movement of macroeconomic,...

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by...

We estimate a multivariate unobserved components stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US Dollar. The empirical model is based on the assumption that both countries' monetary policy strategies may be well described by Taylor rules with a...