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

Abstract

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 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 the long-run dynamic effects are larger for changes in financial uncertainty than macroeconomic uncertainty. Furthermore, we show that the effects of uncertainty differ whether the economy is in a contraction regime or in an expansion regime.