  A methodology is developed for the adjustment of the covariance matrices
underlying a multivariate constant time series dynamic linear model. The
covariance matrices are embedded in a distribution-free inner-product space of
matrix objects which facilitates such adjustment. This approach helps to make
the analysis simple, tractable and robust. To illustrate the methods, a simple
model is developed for a time series representing sales of certain brands of a
product from a cash-and-carry depot. The covariance structure underlying the
model is revised, and the benefits of this revision on first order inferences
are then examined.
