For each time series in the columns of the data matrix,
X, selects an optimal ARMA model (according to an information criteria); then, fits such model and analyses the corresponding residuals. If all the ARMA models are suitable, returns a vector containing the corresponding sums the autocovariances. If some ARMA model is not suitable, it informs the user with a message.
var.cov.sum(X = 1:100, lag.max = 50, p.max = 3, q.max = 3, ic = "BIC", alpha = 0.05, num.lb = 10)