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.

1 2 | ```
var.cov.sum(X = 1:100, lag.max = 50, p.max = 3, q.max = 3, ic = "BIC",
alpha = 0.05, num.lb = 10)
``` |

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