doi:10.3390/econometrics8020020>, details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.">

shrinkTVPVAR: Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage (original) (raw)

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.

Version: 1.0.1
Depends: R (≥ 3.3.0)
Imports: Rcpp, shrinkTVP (≥ 3.1.0), stochvol, coda, methods, grDevices, RColorBrewer, lattice, zoo, mvtnorm
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, shrinkTVP (≥ 3.1.0), stochvol
Suggests: testthat (≥ 3.0.0)
Published: 2025-06-03
DOI: 10.32614/CRAN.package.shrinkTVPVAR
Author: Peter Knaus ORCID iD [aut, cre]
Maintainer: Peter Knaus <peter.knaus at wu.ac.at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: shrinkTVPVAR results

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