BigVAR.intermediate

BigVAR.intermediate-class

It inherits the class BigVAR, and contains the results from rolling validation

Estimates VAR and VARX models with Structured Penalties.

Will Nicholson

BigVAR

Dimension Reduction Methods for Multivariate Time Series

David Matteson

Jacob Bien

BigVAR.intermediate function

<dl>
<dt><code>ZFull</code></dt>
<dd>List containing full lag matrix and time series</dd></dl>
<dt><code>InSampMSFE</code></dt>
<dd>In-sample MSFE from optimal value of lambda</dd>
<dt><code>LambdaGrid</code></dt>
<dd>Grid of candidate lambda values</dd>
<dt><code>index</code></dt>
<dd>Index order of optimal lambda value</dd>
<dt><code>OptimalLambda</code></dt>
<dd>Value of lambda that minimizes MSFE</dd>
<dt><code>Data</code></dt>
<dd>a \(T \times k\) or \(T\times k + m\) multivariate time Series</dd>
<dt><code>lagmax</code></dt>
<dd>Maximal lag order</dd>
<dt><code>Structure</code></dt>
<dd>Penalty structure</dd>
<dt><code>Relaxed</code></dt>
<dd>Indicator for relaxed VAR</dd>
<dt><code>Granularity</code></dt>
<dd>Granularity of penalty grid</dd>
<dt><code>horizon</code></dt>
<dd>Desired forecast horizon</dd>
<dt><code>crossval</code></dt>
<dd>Cross-Validation procedure</dd>
<dt><code>alpha</code></dt>
<dd>additional penalty parameter for Sparse Lag Group or Sparse Own/Other methods. Will contain either the heuristic choice of \(1/(k+1)\) or the value selected by cross validation if the argument <code>dual</code> is set to <code>TRUE</code></dd>
<dt><code>Minnesota</code></dt>
<dd>Minnesota Prior Indicator</dd>
<dt><code>verbose</code></dt>
<dd>verbose indicator</dd>
<dt><code>dual</code></dt>
<dd>indicator as to whether dual cross validation was conducted</dd>
<dt><code>contemp</code></dt>
<dd>indicator if contemporaneous exogenous predictors are used</dd>

Fields

Author

BigVAR.intermediate
This class contains the in-sample results for cv.BigVAR — BigVAR.intermediate

<dl>
<dt><code>ZFull</code></dt>
<dd>List containing full lag matrix and time series</dd>


<dt><code>InSampMSFE</code></dt>
<dd>In-sample MSFE from optimal value of lambda</dd>


<dt><code>LambdaGrid</code></dt>
<dd>Grid of candidate lambda values</dd>


<dt><code>index</code></dt>
<dd>Index order of optimal lambda value</dd>


<dt><code>OptimalLambda</code></dt>
<dd>Value of lambda that minimizes MSFE</dd>


<dt><code>Data</code></dt>
<dd>a \(T \times k\) or \(T\times k + m\) multivariate time Series</dd>


<dt><code>lagmax</code></dt>
<dd>Maximal lag order</dd>


<dt><code>Structure</code></dt>
<dd>Penalty structure</dd>


<dt><code>Relaxed</code></dt>
<dd>Indicator for relaxed VAR</dd>


<dt><code>Granularity</code></dt>
<dd>Granularity of penalty grid</dd>


<dt><code>horizon</code></dt>
<dd>Desired forecast horizon</dd>


<dt><code>crossval</code></dt>
<dd>Cross-Validation procedure</dd>


<dt><code>alpha</code></dt>
<dd>additional penalty parameter for Sparse Lag Group or Sparse Own/Other methods. Will contain either the heuristic choice of \(1/(k+1)\) or the value selected by cross validation if the argument <code>dual</code> is set to <code>TRUE</code></dd>


<dt><code>Minnesota</code></dt>
<dd>Minnesota Prior Indicator</dd>


<dt><code>verbose</code></dt>
<dd>verbose indicator</dd>


<dt><code>dual</code></dt>
<dd>indicator as to whether dual cross validation was conducted</dd>


<dt><code>contemp</code></dt>
<dd>indicator if contemporaneous exogenous predictors are used</dd>


</dl>

BigVAR.intermediate: BigVAR.intermediate This class contains the in-sample results for cv.BigVAR

Description

Arguments

Fields

Author