When the number of cross-sectional observations is large, traditional methods such as VAR or VARMA techniques are not appropriate, since the number of parameters to be estimated grows as the square of the cross-sectional dimension. A possible strategy to achieve parameter reduction is to use factor analytic models since, if the number of common factors is fixed, the number of parameters to estimate grows only linearly with the number of cross-sectional observations. Therefore, they develop a method for the estimation and identification of a factor analytic model when the number of cross-sectional observations is large.