DP12339 Uncertainty Through the Lenses of A Mixed-Frequency Bayesian 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 of the model parameters, and we 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 the eﬀects of uncertainty diﬀer whether the economy is in a contraction regime or in an expansion regime.