stable_bvar

<code>stable_bvar()</code> detects and discards all posterior draws of an
<code>bayesianVARs_bvar</code> object that do not fulfill the stability condition:
A VAR(p) model is considered as stable only if the eigenvalues of the
companion form matrix lie inside the unit circle.

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the
fully Bayesian estimation of vectorautoregressions (VARs) featuring
stochastic volatility (SV). Implements state-of-the-art shrinkage
priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>.
Efficient equation-per-equation estimation following Kastner & Huber
(2020) <doi:10.1002/for.2680> and Carrerio et al. (2021)
<doi:10.1016/j.jeconom.2021.11.010>.

Luis Gruber

bayesianVARs

MCMC Estimation of Bayesian Vectorautoregressions

Stefan Haan

Gregor Kastner

stable_bvar function

<dl><dt>object</dt>
<dd>A <code>bayesianVARs_bvar</code> object obtained via <code>bvar()</code>.</dd>
<dt>quiet</dt>
<dd>logical indicating whether informative output should be omitted.</dd></dl>

Arguments

Stable posterior draws — stable_bvar

<dl>

<dt>object</dt>
<dd>A <code>bayesianVARs_bvar</code> object obtained via <code>bvar()</code>.</dd>


<dt>quiet</dt>
<dd>logical indicating whether informative output should be omitted.</dd>

</dl>

stable_bvar: Stable posterior draws

Description

Usage

Value

Arguments

Examples