aic.ar.wge: AR Model Identification for AR models

In BivinSadler/tswge: Time Series for Data Science

AR Model Identification for AR models

Description

AR model identification using either AIC, AICC, or BIC and MLE, Burg or YW

Usage

aic.ar.wge(x, p = 1:5, type = "aic",method='mle')

Arguments

x	Realization to be analyzed
p	Range of p values to be considered
type	Type of model identification criterion: aic, aicc, or bic
method	Method used for estimation: MLE, Burg, or YW

Value

type	Criterion used: aic (default), aicc, or bic
method	Estimation method used: MLE, Burg, or YW
min_value	Value of the minimized criterion
p	AR order for selected model
phi	AR parameter estimates for selected model
vara	White noise variance estimate for selected model

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig3.18a)
          aic.ar.wge(fig3.18a,p=1:5,type='aicc',method='burg')

BivinSadler/tswge documentation built on Sept. 15, 2023, 4:07 p.m.