Working Paper: CEPR ID: DP12339
Authors: Roberto Casarin; Claudia Foroni; Massimiliano Marcellino; Francesco Ravazzolo
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 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.
Keywords: dynamic panel model; mixed-frequency; markov switching; bayesian inference; mcmc
JEL Codes: C13; C14; C51; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Financial uncertainty (D89) | Macroeconomic variables (E19) |
| Macroeconomic uncertainty (D89) | Macroeconomic variables (E19) |
| Financial uncertainty (D89) | Macroeconomic uncertainty (D89) |
| Ignoring mixed frequency component (C22) | Different results (C29) |
| Ignoring Markov switching mechanism (C22) | Different results (C29) |