Granger causality and regime inference in Bayesian Markov-Switching VARs - Matthieu Droumaguet; Anders Warne; Tomasz Woźniak (European Central Bank (ECB), 2015)
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: C11; C12; C32; C53; E32; 330; Bayesian hypothesis testing; block Metropolis-Hastings sampling; Markov-switching models; mixture models; posterior odds ratio; Kausalanalyse; Markov-Kette; Stichprobenerhebung; VAR-Modell; Schätzung; Industrieproduktion; Geldmenge; USA