VARX Own/Other Sparse Group PenaltySparse refers to not penalizing a whole group. In certain scenarios, a group penalty can be too restrictive. On the other hand, having many groups will substantially increase computation time and generally does not improve forecasting performance.
VARX Own/Other Group PenaltyIn this model the grouping distinguishes between a series’ own lags and those of other series. This structure is similar to Componentwise (see below) but prioritizes “own” lags over “other” lags for a specific lag. This is based on the hypothesis that own lags are more informative than other lags.
VARX Endogenous-FirstVARX Endegenous-First utilizes a penalty to prioritize endogenous series. At a given lag, an exogenous series can enter the model only if their endogenous counterpart is nonzero.
VAR Lag weighted LassoConsists of a Lasso penalty that increases geometrically with lag. This means that shorter lags are prioritized in these models, compared to the set up in other VAR models.
VARX Lag Group LassoGroups the series based on the lags of the explanatory variables. The model selects the variables and their lags based on lag grouping, meaning that the 1st lags, 2nd lags etc. of all variables are put into groups. If not contributing, entire groups will then be penalized.
