aic5.ar.wge

You may select either AIC, AICC, or BIC to use model identification. You can also used ML, Burg, or Yule-Walker estimates. Given a range of values for p and q, the program returns the top 5 candidate models.

 AIC

 MLE 

 AICc 

 Autoregressive 

 Burg 

 BIC 

 AR Model Identification 

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott.  It is helpful for data analysis and for time series instruction.

Bivin Sadler

tswge

Time Series for Data Science

aic5.ar.wge function

<dl> <dt>x</dt>
<dd>Realization to model</dd> <dt>p</dt>
<dd>Range of AR orders to be considered</dd> <dt>type</dt>
<dd>Either 'aic' (default), 'aicc', or 'bic'</dd> <dt>method</dt>
<dd>Either 'MLE' (default), 'Burg', or 'YW'</dd></dl>

Arguments

Author

Return top 5 AIC, AICC, or BIC picks for AR model fits — aic5.ar.wge

<dl>

 <dt>x</dt>
<dd>Realization to model</dd>

 <dt>p</dt>
<dd>Range of AR orders to be considered</dd>

 <dt>type</dt>
<dd>Either 'aic' (default), 'aicc', or 'bic'</dd>

 <dt>method</dt>
<dd>Either 'MLE' (default), 'Burg', or 'YW'</dd>

</dl>

Return top 5 AIC, AICC, or BIC picks for AR model fits

aic5.ar.wge: Return top 5 AIC, AICC, or BIC picks for AR model fits

Description

Usage

Value

Arguments

Author

References

Examples