facloadpairplot

<code>facloadpairplot</code> illustrates the bivariate marginals of the
factor loadings distribution. For a monochrome variant, see
<code>facloadcredplot</code>.

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.

Gregor Kastner

factorstochvol

Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models

Darjus Hosszejni

Luis Gruber

facloadpairplot function

<dl><dt>x</dt>
<dd>Object of class <code>'fsvdraws'</code>, usually resulting from a call
to <code>fsvsample</code>.</dd>
<dt>maxpoints</dt>
<dd>The maximum amount of posterior draws to plot. If
the number of draws stored in <code>x</code> exceeds this number, draws are
thinned accordingly.</dd>
<dt>alpha</dt>
<dd>Level of transparency.</dd>
<dt>cex</dt>
<dd>Controls the size of the dots.</dd></dl>

Arguments

Displays bivariate marginal posterior distributions of factor loadings. — facloadpairplot

<dl>

<dt>x</dt>
<dd>Object of class <code>'fsvdraws'</code>, usually resulting from a call
to <code>fsvsample</code>.</dd>


<dt>maxpoints</dt>
<dd>The maximum amount of posterior draws to plot. If
the number of draws stored in <code>x</code> exceeds this number, draws are
thinned accordingly.</dd>


<dt>alpha</dt>
<dd>Level of transparency.</dd>


<dt>cex</dt>
<dd>Controls the size of the dots.</dd>

</dl>

facloadpairplot: Displays bivariate marginal posterior distributions of factor loadings.

Description

Usage

Value

Arguments

See Also