shrinkTVPVAR: Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with shrinkage priors. Details on the algorithms used are provided in Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.

Version: 0.1.1
Depends: R (≥ 3.3.0)
Imports: Rcpp, shrinkTVP, stochvol, coda, methods, grDevices, RColorBrewer, lattice, zoo
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, shrinkTVP, stochvol
Suggests: testthat (≥ 3.0.0)
Published: 2024-09-16
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
CRAN checks: shrinkTVPVAR results

Documentation:

Reference manual: shrinkTVPVAR.pdf

Downloads:

Package source: shrinkTVPVAR_0.1.1.tar.gz
Windows binaries: r-devel: shrinkTVPVAR_0.1.1.zip, r-release: shrinkTVPVAR_0.1.1.zip, r-oldrel: shrinkTVPVAR_0.1.1.zip
macOS binaries: r-release (arm64): shrinkTVPVAR_0.1.1.tgz, r-oldrel (arm64): shrinkTVPVAR_0.1.1.tgz, r-release (x86_64): shrinkTVPVAR_0.1.1.tgz, r-oldrel (x86_64): shrinkTVPVAR_0.1.1.tgz

Linking:

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