summary.bayesianVARs_bvar

Summary method for <code>bayesianVARs_bvar</code> objects.

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

summary.bayesianVARs_bvar function

<dl><dt>object</dt>
<dd>A <code>bayesianVARs_bvar</code> object obtained via <code>bvar()</code>.</dd>
<dt>quantiles</dt>
<dd>numeric vector which quantiles to compute.</dd>
<dt>digits</dt>
<dd>Single integer indicating the number of decimal places to be
used for rounding the summary statistics. Negative values are not allowed.</dd>
<dt>...</dt>
<dd>Currently ignored!</dd></dl>

Arguments

Summary method for bayesianVARs_bvar objects — summary.bayesianVARs_bvar

<dl>

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


<dt>quantiles</dt>
<dd>numeric vector which quantiles to compute.</dd>


<dt>digits</dt>
<dd>Single integer indicating the number of decimal places to be
used for rounding the summary statistics. Negative values are not allowed.</dd>


<dt>...</dt>
<dd>Currently ignored!</dd>

</dl>

summary.bayesianVARs_bvar: Summary method for bayesianVARs_bvar objects

Description

Usage

Value

Arguments

Examples