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 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...