choose_var

This function computes AIC, FPE, BIC, and HQ up to p = <code>lag_max</code> of VAR model.

Tools to model and forecast multivariate time series
including Bayesian Vector heterogeneous autoregressive (VHAR) model
by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>).
'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Young Geun Kim

bvhar

Bayesian Vector Heterogeneous Autoregressive Modeling

Changryong Baek

choose_var function

<dl><dt>y</dt>
<dd>Time series data of which columns indicate the variables</dd>
<dt>lag_max</dt>
<dd>Maximum Var lag to explore (default = 5)</dd>
<dt>include_mean</dt>
<dd>Add constant term (Default: <code>TRUE</code>) or not (<code>FALSE</code>)</dd>
<dt>parallel</dt>
<dd>Parallel computation using <code><a href="/link/foreach%3A%3Aforeach()?package=bvhar&version=2.2.2" data-mini-rdoc="bvhar::foreach::foreach()">foreach::foreach()</a></code>? By default, <code>FALSE</code>.</dd></dl>

Arguments

Choose the Best VAR based on Information Criteria — choose_var

<dl>

<dt>y</dt>
<dd>Time series data of which columns indicate the variables</dd>


<dt>lag_max</dt>
<dd>Maximum Var lag to explore (default = 5)</dd>


<dt>include_mean</dt>
<dd>Add constant term (Default: <code>TRUE</code>) or not (<code>FALSE</code>)</dd>


<dt>parallel</dt>
<dd>Parallel computation using <code><a href='https://rdrr.io/pkg/foreach/man/foreach.html'>foreach::foreach()</a></code>? By default, <code>FALSE</code>.</dd>

</dl>

choose_var: Choose the Best VAR based on Information Criteria

Description

Usage

Value

Arguments