Learn R Programming

rugarch (version 1.4-2)

ARFIMAfit-class: class: ARFIMA Fit Class

Description

Class for the ARFIMA fit.

Arguments

Slots

fit:

Object of class "vector"

model:

Object of class "vector"

Extends

Class "'>ARFIMA", directly. Class "'>rGARCH", by class "ARFIMA", distance 2.

Methods

coef

signature(object = "ARFIMAfit"): Extracts the coefficients.

fitted

signature(object = "ARFIMAfit"): Extracts the fitted values.

infocriteria

signature(object = "ARFIMAfit"): Calculates and returns various information criteria.

likelihood

signature(object = "ARFIMAfit"): Extracts the likelihood.

residuals

signature(object = "ARFIMAfit"): Extracts the residuals. Optional logical argument standardize (default is FALSE) allows to extract the standardized residuals.

show

signature(object = "ARFIMAfit"): Fit summary.

uncmean

signature(object = "ARFIMAfit"): Calculates and returns the unconditional mean. Takes additional arguments ‘method’ with option for “analytical” or “simulation”, ‘n.sim’ for the number of simulations (if that method was chosen, and defaults to 100000) and ‘rseed’ for the simulation random generator initialization seed.

vcov

signature(object = "ARFIMAfit"): Extracts the covariance matrix of the parameters. Additional logical option of ‘robust’ indicates whether to extract the robust based covariance matrix.

convergence

signature(object = "ARFIMAfit"): Returns the solver convergence code for the fitted object (zero denotes convergence).

reduce

signature(object = "ARFIMAfit"): Zeros parameters (fixing to zero in rugarch is equivalent to eliminating them in estimation) with p-values (optional argument “pvalue”) greater than 0.1 (default), and re-estimates the model. Additional arguments are passed to arfimafit.An additional option “use.robust” (default TRUE) asks whether to use the robust calculated p-values.

getspec

signature(object = "ARFIMAfit"): Extracts and returns the ARFIMA specification from a fitted object.

Examples

Run this code
# NOT RUN {
showClass("ARFIMAfit")
# }

Run the code above in your browser using DataLab