smart_covmat

number of time series in the system.

a symmetric positive definite \((dxd)\) covariance matrix specifying
expected value of the matrix to be generated.

Omega

a positive real number adjusting how close to the given covariance matrix
 the returned individual should be. 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, but for more details read the source code.

accuracy

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

Unconstrained and constrained maximum likelihood estimation of Gaussian Mixture Vector Autoregressive (GMVAR)
model, quantile residual tests, graphical diagnostics, simulations, and forecasting.
Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>.

Savi Virolainen

gmvarkit

Estimate Gaussian Mixture Vector Autoregressive Model

smart_covmat function

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

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

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

Description

Usage

Arguments

Value