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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.