shrinkTVPVAR: Efficient Bayesian Inference for TVP-VAR-SV Models withShrinkage
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 |
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