Select the VAR lag order by information criterion

All candidate lag orders are compared on a common sample: the first max_lag observations are dropped for every candidate so that each VAR(lag) is estimated over the same time window (t = max_lag + 1 .. n). This mirrors statsmodels' VAR.select_order and makes the criteria comparable across lags (otherwise a longer lag would be scored on fewer observations).

Usage

select_var_lag(X, max_lag, criterion = "bic")

Arguments

X

numeric matrix (n_samples x n_features)

max_lag

maximum lag order to consider

criterion

"bic", "aic", "hqic", or "fpe"

Value

the selected lag order (integer)