print.shrinkTVP

Nicer printing of shrinkTVP objects

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the 'shrinkTVPVAR' package.

Peter Knaus

shrinkTVP

Efficient Bayesian Inference for Time-Varying Parameter Models
with Shrinkage

Angela Bitto-Nemling

Annalisa Cadonna

Sylvia Frühwirth-Schnatter

Daniel Winkler

Kemal Dingic

print.shrinkTVP function

<dl><dt>x</dt>
<dd>a <code>shrinkTVP</code> object.</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Peter Knaus <a href="/link/peter.knaus%40wu.ac.at?package=shrinkTVP&version=3.1.0" data-mini-rdoc="shrinkTVP::peter.knaus@wu.ac.at">peter.knaus@wu.ac.at</a>

Author

Nicer printing of shrinkTVP objects — print.shrinkTVP

<dl>

<dt>x</dt>
<dd>a <code>shrinkTVP</code> object.</dd>


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

</dl>

Peter Knaus <a href='mailto:peter.knaus@wu.ac.at'>peter.knaus@wu.ac.at</a>

print.shrinkTVP: Nicer printing of shrinkTVP objects

Description

Usage

Value

Arguments

Author