A Pitfall in Using the Characterization of Granger Non-Causality in Vector Autoregressive Models
Umberto Triacca
Abstract
Open-access reader
Umberto Triacca
Abstract
Open-access reader
It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fails to hold. In these situations Granger non-causality test results must be interpreted with caution.
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It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fails to hold. In these situations Granger non-causality test results must be interpreted with caution.
Key concepts: Granger causality, Autoregressive model, Econometrics, Vector autoregression, Characterization (materials science), Causality (physics), Mathematics, STAR model