print.bayesianVARs_bvar

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

print.bayesianVARs_bvar function

<dl><dt>x</dt>
<dd>Object of class <code>bayesianVARs_bvar</code>, usually resulting from a call
of <code>bvar()</code>.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Pretty printing of a bvar object — print.bayesianVARs_bvar

<dl>

<dt>x</dt>
<dd>Object of class <code>bayesianVARs_bvar</code>, usually resulting from a call
of <code>bvar()</code>.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

print.bayesianVARs_bvar: Pretty printing of a bvar object

Description

Usage

Value

Arguments

Examples