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 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 [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 |
Documentation:
Downloads:
Linking:
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