Temporal aggregation and closure of VARMA models. Some new results

In this paper we examine the effects of temporal aggregation on Vector AutoRegressive Moving Average (VARMA) models. It has relevant implications both in theoretical and empirical domain. Among them we focus the attention on the main consequences of the aggregation (obtained from point in time sampling) on the model identification. Further, under well defined conditions on the model parameters, we explore the closure of the VARMA class (with respect to the temporal aggregation) through theoretical results discussed in proper examples.