is.stable

Using a model estimated by <code>sparseVAR</code>, this function checks whether
the resulting VAR is stable. This is the case, whenever the maximum absolute
eigenvalue of the companion matrix corresponding to the VAR is less than one.
This is sometimes also referred to as that the root lies outside the unit circle.

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

is.stable function

<dl><dt>mod</dt>
<dd>Model estimated using <code>sparseVAR</code>. Can only be a model
with one coefficient vector. Hence, the model must be estimated using a selection
method. See <code>sparseVAR</code> for more details.</dd>
<dt>verbose</dt>
<dd>If <code>TRUE</code>, then the actual maximum absolute
eigenvalue of the companion matrix will be printed to the console.
Default is <code>FALSE</code></dd></dl>

Arguments

Checks whether a VAR is stable — is.stable

<dl>

<dt>mod</dt>
<dd>Model estimated using <code>sparseVAR</code>. Can only be a model
with one coefficient vector. Hence, the model must be estimated using a selection
method. See <code>sparseVAR</code> for more details.</dd>


<dt>verbose</dt>
<dd>If <code>TRUE</code>, then the actual maximum absolute
eigenvalue of the companion matrix will be printed to the console.
Default is <code>FALSE</code></dd>

</dl>

is.stable: Checks whether a VAR is stable

Description

Usage

Value

Arguments