Temporal aggregation and spurious instantaneous causality in multiple time series models

Abstract

Large aggregation interval asymptotics are used to investigate the relation between Granger causality in disaggregated vector autoregressions (VARs) and associated contemporaneous correlation among innovations of the aggregated system. One of our main contributions is that we outline various conditions under which the informational content of error covariance matrices yields insight into the causal structure of the VAR. Monte Carlo results suggest that our asymptotic findings are applicable even when the aggregation interval is small, as long as the time series are not characterized by high levels of persistence.