Abstract
Factor Models are extensively used as a resource to reduce the dimensionality (number of variables) and to explain the common variability in multivariate data sets. The idea, like in Principal Component analysis, is to consider a small set of factors, which explain most of the variance in the data. In the context of multivariate time series, the analysis of the covariance structure become…