Macroeconomic uncertainty through the lenses of a mixed-frequency panel Markov-switching model

We propose a Bayesian panel model for mixed frequency data, where parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. To estimate the model, we develop a Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution, and we assess its performance in simulation experiments. We use the model to study the effects of macroeconomic uncer-tainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that there are large differences in the effects of uncertainty in the contraction regime and the expansion regime. The use of mixed frequency data amplifies the rel-evance of the asymmetry. Financial uncertainty plays a more important role than macroeconomic uncertainty, and its effects are also more homogeneous across variables and countries. Disregarding either the mixed-frequency com-ponent or the Markov-switching mechanism can bring to substantially differ-ent results.