Granger causality and regime inference in Markov Switching VAR models with Bayesian methods
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0883-7252; 1099-1255
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Journal of applied econometrics, 2017, Vol. 32, No. 4, pp. 802-818
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DROUMAGUET, Matthieu, WARNE, Anders, WOŹNIAK, Tomasz, Granger causality and regime inference in Markov Switching VAR models with Bayesian methods, Journal of applied econometrics, 2017, Vol. 32, No. 4, pp. 802-818 - https://hdl.handle.net/1814/59586
Abstract
In this paper, we derive restrictions for Granger noncausality in MS-VAR models and show under what conditions a variable does not affect the forecast of the hidden Markov process. To assess the noncausality hypotheses, we apply Bayesian inference. The computational tools include a novel block Metropolis-Hastings sampling algorithm for the estimation of the underlying models. We analyze a system of monthly US data on money and income. The results of testing in MS-VARs contradict those obtained with linear VARs: the money aggregate M1 helps in forecasting industrial production and in predicting the next period's state. Copyright (c) 2016 John Wiley & Sons, Ltd.
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First published: 27 June 2016