Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model

We propose a Bayesian panel model for mixed frequency data whose parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. We develop a proper Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters and test its properties in simulation experiments. We use the model to study the effects of macroeconomic uncertainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that for most of the variables financial uncertainty dominates macroeconomic uncertainty. Furthermore, we show that uncertainty coefficients differ if the economy is in a contraction regime or in an expansion regime.