est.garma.wge: Estimate the parameters of a GARMA model.

In tswge: Time Series for Data Science

Estimate the parameters of a GARMA model.

Description

This function uses the grid search algorithm discussed in Section 11.5 of Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

est.garma.wge(x,low.u,low.lambda,high.u,high.lambda,inc.u,inc.lambda,p.max,nback=500)

Arguments

x	Realization to be analyzed
low.u	The lower limit for u in the grid search
low.lambda	The lower limit for lambda in the grid search
high.u	The upper limit for u in the grid search
high.lambda	The upper limit for lambda in the grid search
inc.u	The increment, e.g. .01, .001, etc. in the grid search on possible u values
inc.lambda	The increment, e.g. .01, .001, etc. in the grid search on possible lambda values
p.max	Maximum value of p allowed for the AR component of the model
nback	Number of backcasts to be used (see section 11.5 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Details

We assume q=0 and do not allow moving average terms in the model.

Value

u	Estimate of u
lambda	Estimate of lambda
phi	Estimates of the pth order AR component of the model where p is some integer from 0 to p.max
vara	The estimated white noise variance
aic	The aic value associated with the final model

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott. See also Hosking (1984), Gray, Zhang, and Woodward(1989), and Woodward, Cheng, and Gray(1998)

Examples

data(llynx)
est.garma.wge(llynx,low.u=.4,high.u=.9,low.lambda=.2,high.lambda=.4,inc.u=.01,inc.lambda=.1,p.max=1)

tswge documentation built on June 8, 2025, 1:25 p.m.