Granger Causality and Regime Inference in Markov Switching VAR Models with Bayesian Methods | Zendy

Droumaguet Matthieu | Zendy; Warne Anders | Zendy; Woźniak Tomasz | Zendy

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Granger Causality and Regime Inference in Markov Switching VAR Models with Bayesian Methods

Author(s) -

Droumaguet Matthieu,

Warne Anders,

Woźniak Tomasz

Publication year - 2016

Publication title -

journal of applied econometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.878

H-Index - 99

eISSN - 1099-1255

pISSN - 0883-7252

DOI - 10.1002/jae.2531

Subject(s) - granger causality , econometrics , bayesian vector autoregression , inference , bayesian probability , markov chain , causality (physics) , bayesian inference , vector autoregression , computer science , economics , artificial intelligence , machine learning , physics , quantum mechanics

Summary 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 © 2016 John Wiley & Sons, Ltd.

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