smart_covmat

<code>smart_covmat</code> generates random VAR model \((dxd)\) error term covariance matrix \(\Omega\)
 from (scaled) Wishart distribution that is fairly close to the given matrix.

internal

Penalized and non-penalized maximum likelihood estimation of smooth
transition vector autoregressive models with various types of transition weight
functions, conditional distributions, and identification methods. Constrained
estimation with various types of constraints is available. Residual based
model diagnostics, forecasting, simulations, and calculation of impulse
response functions, generalized impulse response functions, and generalized
forecast error variance decompositions. See
Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

smart_covmat function

<dl><dt>Omega</dt>
<dd>a symmetric positive definite \((dxd)\) covariance matrix specifying
expected value of the matrix to be generated.</dd>
<dt>accuracy</dt>
<dd>a positive real number adjusting how close to the given covariance matrix
 the returned individual should be.
The standard deviation of each diagonal element is...<ul>
<li>\(\omega_{i,i}/\)<code>accuracy</code> when <code>accuracy &gt; d/2</code></li>
<li>and <code>sqrt(2/d)*</code>\(\omega_{i,i}\) when <code>accuracy &lt;= d/2</code>.</li>
</ul>Wishart distribution is used for reduced form models, but for more details read the source code.</dd></dl>

Arguments

Create random VAR model \((dxd)\) error term covariance matrix \(\Omega\)
  fairly close to the given positive definite covariance matrix using (scaled)
  Wishart distribution — smart_covmat

<dl>

<dt>Omega</dt>
<dd>a symmetric positive definite \((dxd)\) covariance matrix specifying
expected value of the matrix to be generated.</dd>


<dt>accuracy</dt>
<dd>a positive real number adjusting how close to the given covariance matrix
 the returned individual should be.
The standard deviation of each diagonal element is...<ul>
<li>\(\omega_{i,i}/\)<code>accuracy</code> when <code>accuracy &gt; d/2</code></li>
<li>and <code>sqrt(2/d)*</code>\(\omega_{i,i}\) when <code>accuracy &lt;= d/2</code>.</li>
</ul>Wishart distribution is used for reduced form models, but for more details read the source code.</dd>

</dl>

Create random VAR model \((dxd)\) error term covariance matrix \(\Omega\)
 fairly close to the given positive definite covariance matrix using (scaled)
 Wishart distribution

smart_covmat: Create random VAR model \((dxd)\) error term covariance matrix \(\Omega\) fairly close to the given positive definite covariance matrix using (scaled) Wishart distribution

Description

Usage

Value

Arguments