  Simple exponential smoothing is widely used in forecasting economic time
series. This is because it is quick to compute and it generally delivers
accurate forecasts. On the other hand, its multivariate version has received
little attention due to the complications arising with the estimation. Indeed,
standard multivariate maximum likelihood methods are affected by numerical
convergence issues and bad complexity, growing with the dimensionality of the
model. In this paper, we introduce a new estimation strategy for multivariate
exponential smoothing, based on aggregating its observations into scalar models
and estimating them. The original high-dimensional maximum likelihood problem
is broken down into several univariate ones, which are easier to solve.
Contrary to the multivariate maximum likelihood approach, the suggested
algorithm does not suffer heavily from the dimensionality of the model. The
method can be used for time series forecasting. In addition, simulation results
show that our approach performs at least as well as a maximum likelihood
estimator on the underlying VMA(1) representation, at least in our test
problems.
