Granger Causality and Regime Inference in Bayesian Markov-Switching VARs

52 Pages Posted: 23 Jun 2015

See all articles by Matthieu Droumaguet

Matthieu Droumaguet

European University Institute

Anders Warne

European Central Bank (ECB)

Tomasz Wozniak

University of Melbourne - Department of Economics

Date Written: June 22, 2015

Abstract

We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for posterior inference include a novel block Metropolis-Hastings sampling algorithm for the estimation of the restricted models. We analyze a system of monthly US data on money and income. The test results in MS-VARs contradict those in linear VARs: the money aggregate M1 is useful for forecasting income and for predicting the next period’s state.

Keywords: Bayesian hypothesis testing; block Metropolis-Hastings sampling; Markov-switching models; mixture models; posterior odds ratio

JEL Classification: C11, C12, C32, C53, E32

Suggested Citation

Droumaguet, Matthieu and Warne, Anders and Wozniak, Tomasz, Granger Causality and Regime Inference in Bayesian Markov-Switching VARs (June 22, 2015). ECB Working Paper No. 1794, Available at SSRN: https://ssrn.com/abstract=2621511 or http://dx.doi.org/10.2139/ssrn.2621511

Matthieu Droumaguet

European University Institute ( email )

Villa Schifanoia
133 via Bocaccio
Firenze (Florence), Tuscany 50014
Italy

Anders Warne

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Tomasz Wozniak (Contact Author)

University of Melbourne - Department of Economics ( email )

Melbourne, 3010
Australia

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