Method for performing rolling estimation of the GO-GARCH model.
gogarchroll(spec, data, n.ahead = 1, forecast.length = 50, n.start = NULL,
refit.every = 25, refit.window = c("recursive", "moving"), window.size = NULL,
solver = "solnp", solver.control = list(), fit.control = list(), rseed = NULL,
cluster = NULL, save.fit = FALSE, save.wdir = NULL, ...)A multivariate data object. Can be a matrix or data.frame or timeSeries.
The forecast horizon (only 1-ahead supported for rolling forecasts).
The length of the total forecast for which out of sample data from the dataset will be excluded for testing.
Instead of forecast.length, this determines the starting point in the dataset from which to initialize the rolling forecast.
Determines every how many periods the model is re-estimated.
Whether the refit is done on an expanding window including all the previous data or a moving window where all previous data is used for the first estimation and then moved by a length equal to refit.every (unless the window.size option is used instead).
If not NULL, determines the size of the moving window in the rolling estimation, which also determines the first point used.
The solver to use.
Control parameters parameters passed to the fitting function.
Control parameters passed to the solver.
Initialization seed for first ICA fit. The rest of the ICA fits are initialized with the previous mixing matrix (using A.init).
A cluster object created by calling makeCluster from
the parallel package. If it is not NULL, then this will be used for parallel
estimation (remember to stop the cluster on completion).
Whether to save the fitted objects of class '>goGARCHfit
during the estimation of each (“refit.every”). If true, the directory to
save must be provided (see below). The function will not save this by default
for reasons of memory management, but can save it as an “.rda” file in
the user's chosen directory for further analysis.
.