The Annals of Applied Statistics

Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model

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

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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, 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.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 4 (2018), 2559-2586.

Dates
Received: January 2017
Revised: April 2018
First available in Project Euclid: 13 November 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1542078056

Digital Object Identifier
doi:10.1214/18-AOAS1168

Mathematical Reviews number (MathSciNet)
MR3875712

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

Citation

Casarin, Roberto; Foroni, Claudia; Marcellino, Massimiliano; Ravazzolo, Francesco. Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model. Ann. Appl. Stat. 12 (2018), no. 4, 2559--2586. doi:10.1214/18-AOAS1168. https://projecteuclid.org/euclid.aoas/1542078056


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Supplemental materials

  • Supplement B: Computational details. This document contains the derivation of the full conditional distributions of the Gibbs sampler and a description of the sampling methods used.
  • Supplement C: Simulation results. This document reports the results of the simulation experiments used to check the efficiency of the proposed MCMC procedure.
  • Supplement D: Data description. This document contains a description and a preliminary analysis of the data used in the empirical application.
  • Supplement E: Further empirical results. This document provides further empirical results and robustness checks.