aic.ar.wge | R Documentation |
AR model identification using either AIC, AICC, or BIC and MLE, Burg or YW
aic.ar.wge(x, p = 1:5, type = "aic",method='mle')
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 |
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 |
Wayne Woodward
"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott
data(fig3.18a) aic.ar.wge(fig3.18a,p=1:5,type='aicc',method='burg')
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