Vector autoregressive models with measurement errors for testing ganger causality

This paper develops a method for estimating parameters of a vector autoregression (VAR) observed in white noise. The estimation method assumes the noise variance matrix is known and does not require any iterative process. This study provides consistent estimators and the asymptotic distribution of the parameters required for conducting tests of Granger causality. The applicability and usefulness of the proposed approach are illustrated using a functional magnetic resonance imaging dataset. The application is not presented in this extended abstract.