Open Access
December 2018 Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model
Roberto Casarin, Claudia Foroni, Massimiliano Marcellino, Francesco Ravazzolo
Ann. Appl. Stat. 12(4): 2559-2586 (December 2018). DOI: 10.1214/18-AOAS1168


 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 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 relevance 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 component or the Markov-switching mechanism can bring to substantially different results.


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Roberto Casarin. Claudia Foroni. Massimiliano Marcellino. Francesco Ravazzolo. "Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model." Ann. Appl. Stat. 12 (4) 2559 - 2586, December 2018.


Received: 1 January 2017; Revised: 1 April 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029466
MathSciNet: MR3875712
Digital Object Identifier: 10.1214/18-AOAS1168

Keywords: Bayesian inference , Dynamic panel models , econometrics , Markov chain Monte Carlo , Markov-switching , mixed-frequency

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.12 • No. 4 • December 2018
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