Tuning Parameters for GARCHA Models

garch_order(range = c(0L, 3L), trans = NULL)

arch_order(range = c(0L, 3L), trans = NULL)

mgarch_order(range = c(0L, 3L), trans = NULL)

garch_t_student()

asymmetry()

Arguments

range	A two-element vector holding the defaults for the smallest and largest possible values, respectively.
trans	A `trans` object from the `scales` package, such as `scales::log10_trans()` or `scales::reciprocal_trans()`. If not provided, the default is used which matches the units used in `range`. If no transformation, `NULL`.

Value

A parameter

Details

The main parameters for GARCHA models are:

garch_order: Integer with the garch order.
arch_order: Integer with the arch_order.
mgarch_order: Integer with the mgarch order.
garch_t_student: A boolean value to specify for a generalized t-student garch model.
asymmetry: a string value for the asymmetric function for an asymmetric GARCH process. By default the value "none" for standard GARCH process. If "logit" a logistic function is used for asymmetry, and if "exp" an exponential function is used.
non_seasonal_ar: The order of the non-seasonal auto-regressive (AR) terms.
non_seasonal_ma: The order of the non-seasonal moving average (MA) terms.
markov_chains: The number of markov chains.
adapt_delta: The thin of the jumps in a HMC method
tree_depth: Maximum depth of the trees

Examples

non_seasonal_ar()
#> Non-seasonal AR Term (quantitative)
#> Range: [0, 5]

non_seasonal_differences()
#> Non-seasonal Differencing Term (quantitative)
#> Range: [0, 2]

non_seasonal_ma()
#> Non-seasonal MA Term (quantitative)
#> Range: [0, 5]