AIC.tsgarch.estimate

Extract the AIC from an estimated model.

Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.

Alexios Ghalanos

tsgarch

Univariate GARCH Models

Alexios Galanos

AIC.tsgarch.estimate function

<dl><dt>object</dt>
<dd>an object of class &#8220;tsgarch.estimate&#8221;.</dd>
<dt>...</dt>
<dd>not currently used.</dd>
<dt>k</dt>
<dd>the penalty per parameter to be used; the default k = 2 is the
classical AIC.</dd></dl>

Arguments

Akaike's An Information Criterion — AIC.tsgarch.estimate

<dl>

<dt>object</dt>
<dd>an object of class &#8220;tsgarch.estimate&#8221;.</dd>


<dt>...</dt>
<dd>not currently used.</dd>


<dt>k</dt>
<dd>the penalty per parameter to be used; the default k = 2 is the
classical AIC.</dd>

</dl>

AIC.tsgarch.estimate: Akaike's An Information Criterion

Description

Usage

Value

Arguments