garch_params: Tuning Parameters for GARCHA Models

In bayesmodels: The 'Tidymodels' Extension for Bayesian Models

Description

Tuning Parameters for GARCHA Models

Usage

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.

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

Value

A parameter

A parameter

A parameter

A parameter

A parameter

Examples

non_seasonal_ar()

non_seasonal_differences()

non_seasonal_ma()

bayesmodels documentation built on June 29, 2021, 1:06 a.m.