cvVAR_ENET

This internal function performs cross validation using elastic net (ENET)
estimation via the <code>glmnet</code> package. It supports parallel processing if requested.

internal

Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024)
<doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional
Vector Auto-regressive Models". This package provides tools for detecting change points
in the transition matrices of VAR models, effectively identifying shifts in temporal
and cross-correlations within high-dimensional time series data.

Yuhan Tian

VARcpDetectOnline

Sequential Change Point Detection for High-Dimensional VAR
Models

Abolfazl Safikhani

cvVAR_ENET function

<dl><dt>X</dt>
<dd>A numeric matrix of predictors.</dd>
<dt>y</dt>
<dd>Numeric vector of responses.</dd>
<dt>nvar</dt>
<dd>Integer. The number of variables in the original VAR (number of columns in data).</dd>
<dt>opt</dt>
<dd>List. A list of options including:<ul>
<li><code>alpha</code>: The elastic net mixing parameter (default = 1).</li>
<li><code>nlambda</code>: Number of lambda values (default = 100).</li>
<li><code>type.measure</code>: Error measure for CV (default = "mse").</li>
<li><code>nfolds</code>: Number of folds for CV (default = 10).</li>
<li><code>parallel</code>: Logical. Whether to use parallel processing (default = FALSE).</li>
<li><code>ncores</code>: Number of cores for parallel processing (default = 1).</li>
<li><code>lambdas_list</code>: Optionally, a user-specified list of lambdas.</li>
<li><code>folds_ids</code>: Optionally, user-specified fold IDs for CV.</li>
</ul></dd></dl>

Arguments

Cross Validation for Elastic Net VAR Estimation — cvVAR_ENET

<dl>

<dt>X</dt>
<dd>A numeric matrix of predictors.</dd>


<dt>y</dt>
<dd>Numeric vector of responses.</dd>


<dt>nvar</dt>
<dd>Integer. The number of variables in the original VAR (number of columns in data).</dd>


<dt>opt</dt>
<dd>List. A list of options including:<ul>
<li><code>alpha</code>: The elastic net mixing parameter (default = 1).</li>
<li><code>nlambda</code>: Number of lambda values (default = 100).</li>
<li><code>type.measure</code>: Error measure for CV (default = "mse").</li>
<li><code>nfolds</code>: Number of folds for CV (default = 10).</li>
<li><code>parallel</code>: Logical. Whether to use parallel processing (default = FALSE).</li>
<li><code>ncores</code>: Number of cores for parallel processing (default = 1).</li>
<li><code>lambdas_list</code>: Optionally, a user-specified list of lambdas.</li>
<li><code>folds_ids</code>: Optionally, user-specified fold IDs for CV.</li>
</ul></dd>

</dl>

cvVAR_ENET: Cross Validation for Elastic Net VAR Estimation

Description

Usage

Value

Arguments