| aic.wge | R Documentation | 
ARMA model identification using either AIC, AICC, or BIC
aic.wge(x, p = 0:5, q = 0:2, type = "aic")| x | Realization to be analyzed | 
| p | Range of p values to be considered | 
| q | Range of q values to be considered | 
| type | Type of model identification criterion: aic, aicc, or bic | 
| type | Criterion used: aic (default), aicc, or bic | 
| min_value | Value of the minimized criterion | 
| p | AR order for selected model | 
| phi | AR parameter estimates for selected model | 
| q | MA order for selected model | 
| theta | MA 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.wge(fig3.18a,p=0:5,q=0:1,type='aicc')Add the following code to your website.
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