Nowcasting causality in mixed frequency vector autoregressive models

Learning coherent Granger-causality in panel vector autoregressive models

We consider the problem of forecasting multiple time series across multiple cross-sections based solely on the past observations of the series. We propose to use panel vector autoregressive model to capture the inter-dependencies on the past values of the multiple series. We restrict the panel vector autoregressive model to exclude the cross-sectional relationships and propose a method to learn...

BOUNDED INFLUENCE APPROACHES TO CONSTRAINED MIXED VECTOR AUTOREGRESSIVE MODELS by

Investigating brain connectivity using mixed effects vector autoregressive models

We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misle...

Vector Autoregressive Models

A Pitfall in Using the Characterization of Granger Non-Causality in Vector Autoregressive Models

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 fail...