dlogmultinorm

<code>dlogmultinorm</code> calculates logarithms of multiple multivariate normal
 densities with varying mean and constant covariance matrix.

internal

Unconstrained and constrained maximum likelihood estimation of structural and reduced form
Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t
mixture vector autoregressive models, quantile residual tests, graphical diagnostics,
simulations, forecasting, and estimation of generalized impulse response function and generalized
forecast error variance decomposition.
Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>,
Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.

Savi Virolainen

gmvarkit

Estimate Gaussian and Student's t Mixture Vector Autoregressive
Models

dlogmultinorm function

<dl><dt>y</dt>
<dd>dimension \((T x k)\) matrix where each row is a k-dimensional vector</dd>
<dt>mu</dt>
<dd>dimension \((T x k)\) matrix where each row is the mean of the k-dimensional
vector in corresponding row of <code>y</code>.</dd>
<dt>inv_Omega</dt>
<dd>inverse of the \((k x k)\) covariance matrix Omega.</dd>
<dt>log_det_Omega</dt>
<dd>logarithm of the determinant of the covariance matrix Omega.</dd></dl>

Arguments

Calculate logarithms of multiple multivariate normal densities with varying
 mean and constant covariance matrix — dlogmultinorm

<dl>

<dt>y</dt>
<dd>dimension \((T x k)\) matrix where each row is a k-dimensional vector</dd>


<dt>mu</dt>
<dd>dimension \((T x k)\) matrix where each row is the mean of the k-dimensional
vector in corresponding row of <code>y</code>.</dd>


<dt>inv_Omega</dt>
<dd>inverse of the \((k x k)\) covariance matrix Omega.</dd>


<dt>log_det_Omega</dt>
<dd>logarithm of the determinant of the covariance matrix Omega.</dd>

</dl>

dlogmultinorm: Calculate logarithms of multiple multivariate normal densities with varying mean and constant covariance matrix

Description

Usage

Value

Arguments