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 | 
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