get_IC

<code>get_IC</code> calculates the information criteria values
 AIC, HQIC, and BIC divided by the number of observations.

internal

Maximum likelihood estimation of smooth transition vector
autoregressive models with various types of transition weight functions,
conditional distributions, and identification methods. Constrained
estimation with various types of constraints is available. Residual based
model diagnostics, forecasting, simulations, and calculation of impulse
response functions, generalized impulse response functions, and generalized
forecast error variance decompositions. See
Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
Markku Lanne, Savi Virolainen (2024) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

get_IC function

<dl><dt>loglik</dt>
<dd>log-likelihood value</dd>
<dt>npars</dt>
<dd>number of (freely estimated) parameters in the model</dd>
<dt>T_obs</dt>
<dd>numbers of observations with the \(p\) starting values excluded.</dd></dl>

Arguments

Calculate AIC, HQIC, and BIC — get_IC

<dl>

<dt>loglik</dt>
<dd>log-likelihood value</dd>


<dt>npars</dt>
<dd>number of (freely estimated) parameters in the model</dd>


<dt>T_obs</dt>
<dd>numbers of observations with the \(p\) starting values excluded.</dd>

</dl>

get_IC: Calculate AIC, HQIC, and BIC

Description

Usage

Value

Arguments