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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 12339, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 financiall uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. Wefind 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: Bayesian inference; dynamic panel model; Markov switching; MCMC; mixed-frequency (search for similar items in EconPapers)
JEL-codes: C13 C14 C51 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
Date: 2017-09
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Working Paper: Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model (2016) Downloads
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