warn_eigens

<code>warn_ar_roots</code> warns if the model contains near-unit-roots in some regimes

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

warn_eigens function

<dl><dt>gsmvar</dt>
<dd>an object of class <code>'gsmvar'</code>, typically created with <code>fitGSMVAR</code> or <code>GSMVAR</code>.</dd>
<dt>stat_tol</dt>
<dd>numerical tolerance for stationarity of the AR parameters: if the "bold A" matrix of any regime
has eigenvalues larger that <code>1 - stat_tol</code> the model is classified as non-stationary. Note that if the
tolerance is too small, numerical evaluation of the log-likelihood might fail and cause error.</dd>
<dt>posdef_tol</dt>
<dd>numerical tolerance for positive definiteness of the error term covariance matrices: if
the error term covariance matrix of any regime has eigenvalues smaller than this, the model is classified
as not satisfying positive definiteness assumption. Note that if the tolerance is too small, numerical
evaluation of the log-likelihood might fail and cause error.</dd></dl>

Arguments

Warn about near-unit-roots in some regimes — warn_eigens

<dl>

<dt>gsmvar</dt>
<dd>an object of class <code>'gsmvar'</code>, typically created with <code>fitGSMVAR</code> or <code>GSMVAR</code>.</dd>


<dt>stat_tol</dt>
<dd>numerical tolerance for stationarity of the AR parameters: if the "bold A" matrix of any regime
has eigenvalues larger that <code>1 - stat_tol</code> the model is classified as non-stationary. Note that if the
tolerance is too small, numerical evaluation of the log-likelihood might fail and cause error.</dd>


<dt>posdef_tol</dt>
<dd>numerical tolerance for positive definiteness of the error term covariance matrices: if
the error term covariance matrix of any regime has eigenvalues smaller than this, the model is classified
as not satisfying positive definiteness assumption. Note that if the tolerance is too small, numerical
evaluation of the log-likelihood might fail and cause error.</dd>

</dl>

warn_eigens: Warn about near-unit-roots in some regimes

Description

Usage

Value

Arguments

Details