aic.ar.wge

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

AICc

Autoregressive

Burg

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

aic.ar.wge function

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

Arguments

Author

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

<dl>

 <dt>x</dt>
<dd>Realization to be analyzed</dd>

 <dt>p</dt>
<dd>Range of p values to be considered</dd>

 <dt>type</dt>
<dd>Type of model identification criterion: aic, aicc, or bic</dd>

 <dt>method</dt>
<dd>Method used for estimation: MLE, Burg, or YW</dd>

</dl>

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

Description

Usage

Value

Arguments

Author

References

Examples