my_gig

Vectorized version of <code>rgig</code>

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

my_gig function

<dl><dt>n</dt>
<dd>A single integer indicating the number of draws to generate.</dd>
<dt>lambda</dt>
<dd>vector of shape parameters.</dd>
<dt>chi</dt>
<dd>vector of shape/scale parameters. Must be nonnegative for positive lambdas and positive else.</dd>
<dt>psi</dt>
<dd>vector of shape/scale parameters. Must be nonnegative for negative lambdas and positive else.</dd></dl>

Arguments

Draw from generalized inverse Gaussian — my_gig

<dl>

<dt>n</dt>
<dd>A single integer indicating the number of draws to generate.</dd>


<dt>lambda</dt>
<dd>vector of shape parameters.</dd>


<dt>chi</dt>
<dd>vector of shape/scale parameters. Must be nonnegative for positive lambdas and positive else.</dd>


<dt>psi</dt>
<dd>vector of shape/scale parameters. Must be nonnegative for negative lambdas and positive else.</dd>

</dl>

my_gig: Draw from generalized inverse Gaussian

Description

Usage

Value

Arguments

Examples