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

Roberto Casarin, Claudia Foroni, Massimiliano Marcellino and Francesco Ravazzolo ()

No 585, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

Abstract: We propose a Bayesian panel model for mixed frequency data whose parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. We develop a proper Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters and 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 uncertainty coefficients differ if the economy is in a contraction regime or in an expansion regime. JEL codes: C13, C14, C51, C53. Keywords: dynamic panel model, mixed-frequency, Markov switching, Bayesian inference, MCMC.

New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2016
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Working Paper: Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model (2017) Downloads
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