ic_selection

Selects the optimal penalty parameter using information criteria

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Ines Wilms

bigtime

Sparse Estimation of Large Time Series Models

David S. Matteson

Jacob Bien

Sumanta Basu

Will Nicholson

Enrico Wegner

ic_selection function

<dl><dt>mod</dt>
<dd>Model estimated Model estimated using <code>sparseVAR</code>,
<code>sparseVARX</code>, or <code>sparseVARMA</code></dd>
<dt>ic</dt>
<dd>Which information criteria should be used. Must be one of
<code>"bic"</code>, <code>"aic"</code> or <code>"hq"</code></dd>
<dt>verbose</dt>
<dd>If true, some useful information will be printed during the process</dd></dl>

Arguments

Selects the optimal penalty parameter using information criteria — ic_selection

<dl>

<dt>mod</dt>
<dd>Model estimated Model estimated using <code>sparseVAR</code>,
<code>sparseVARX</code>, or <code>sparseVARMA</code></dd>


<dt>ic</dt>
<dd>Which information criteria should be used. Must be one of
<code>"bic"</code>, <code>"aic"</code> or <code>"hq"</code></dd>


<dt>verbose</dt>
<dd>If true, some useful information will be printed during the process</dd>

</dl>

ic_selection: Selects the optimal penalty parameter using information criteria

Description

Usage

Value

Arguments