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 the long-run dynamic effects are larger for changes in financial uncertainty than macroeconomic uncertainty. Furthermore, we show that the effects of uncertainty differ whether the economy is in a contraction regime or in an expansion regime.

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.

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

Roberto Casarin;Francesco Ravazzolo
2018-01-01

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 the long-run dynamic effects are larger for changes in financial uncertainty than macroeconomic uncertainty. Furthermore, we show that the effects of uncertainty differ whether the economy is in a contraction regime or in an expansion regime.
File in questo prodotto:
File Dimensione Formato  
RevCFMR_04012018ims.pdf

non disponibili

Descrizione: panelmidas
Tipologia: Documento in Pre-print
Licenza: Accesso chiuso-personale
Dimensione 642.69 kB
Formato Adobe PDF
642.69 kB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3697773
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 9
social impact