Skip to contents

Specify which vine copula models are fitted and how often they are refit as well as how big the training data set is. Remember that the estimation process is done in a rolling window fashion and the arguments (train and refit size) will have to match with the arguments of the also to be specified marginal_settings.

Usage

vine_settings(train_size, refit_size, family_set = "all", vine_type = "rvine")

# S4 method for vine_settings
show(object)

Arguments

train_size

equivalent to the slot definition below

refit_size

equivalent to the slot definition below

family_set

equivalent to the slot definition below

vine_type

equivalent to the slot definition below

object

An object of class vine_settings

Value

Object of class vine_settings

Functions

  • vine_settings(): Class constructor taking the arguments specified in the slots below

Slots

train_size

Positive count specifying the training data size.

refit_size

Positive count specifying for how many periods a vine is used

family_set

Character vector specifying the family of copulas that are used. For possible choices see rvinecopulib::bicop. Note for conditional sampling just parametric copula families are possible so do not use the family arguments all and tll.

vine_type

character value that specifies which vine class should be fitted. Possible choices right now are rvine (regular vine) and dvine (drawable vine).

Examples

# the most basic initialization
vine_settings(100, 25)
#> An object of class <vine_settings>
#> train_size: 100 
#> refit_size: 25 
#> family_set: all 
#> vine_type: rvine 
# some individual note
vine_settings(
  train_size = 100, refit_size = 20,
  family_set = c("gumbel", "joe"),
  vine_type = "dvine"
)
#> An object of class <vine_settings>
#> train_size: 100 
#> refit_size: 20 
#> family_set: gumbel joe 
#> vine_type: dvine