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HomeGranger causality and regime inference in Bayesian Markov-Switching VARs
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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.
- Published:
- Corporate author(s): European Central Bank
- Themes: Economy — Finance
- Subject: econometrics , economic analysis , economic cycle , economic forecasting , economic model
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| ISSN 1725-2806 | ISBN 978-92-899-1607-3 | DOI 10.2866/845677 | Catalogue number QB-AR-15-034-EN-N |
- Released on EU publications website:
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