Bayesian Testing of Granger Causality in Markov-Switching VARs

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Show simple item record DROUMAGUET, Matthieu WOŹNIAK, Tomasz 2012-03-02T11:35:11Z 2012-03-02T11:35:11Z 2012
dc.identifier.issn 1725-6704
dc.description.abstract Recent economic developments have shown the importance of spillover and contagion effects in financial markets as well as in macroeconomic reality. Such effects are not limited to relations between the levels of variables but also impact on the volatility and the distributions. We propose a method of testing restrictions for Granger noncausality on all these levels in the framework of Markov-switching Vector Autoregressive Models. The conditions for Granger noncausality for these models were derived by Warne (2000). Due to the nonlinearity of the restrictions, classical tests have limited use. We, therefore, choose a Bayesian approach to testing. The inference consists of a novel Gibbs sampling algorithm for estimation of the restricted models, and of standard methods of computing the Posterior Odds Ratio. The analysis may be applied to financial and macroeconomic time series with complicated properties, such as changes of parameter values over time and heteroskedasticity. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2012/06 en
dc.rights info:eu-repo/semantics/openAccess
dc.subject Granger Causality en
dc.subject Markov Switching Models en
dc.subject Hypothesis Testing en
dc.subject Posterior Odds Ratio en
dc.subject Gibbs Sampling en
dc.title Bayesian Testing of Granger Causality in Markov-Switching VARs en
dc.type Working Paper en
dc.neeo.contributor DROUMAGUET|Matthieu|aut|
dc.neeo.contributor WOZNIAK|Tomasz|aut|
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