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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>.

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VersionRepositoryFileSize
1.0.1 rolling linux/jammy R-4.5 shrinkTVPVAR_1.0.1.tar.gz 319.0 KiB
1.0.1 rolling linux/noble R-4.5 shrinkTVPVAR_1.0.1.tar.gz 335.5 KiB
1.0.1 rolling source/ R- shrinkTVPVAR_1.0.1.tar.gz 52.3 KiB
1.0.1 latest linux/jammy R-4.5 shrinkTVPVAR_1.0.1.tar.gz 319.0 KiB
1.0.1 latest linux/noble R-4.5 shrinkTVPVAR_1.0.1.tar.gz 335.5 KiB
1.0.1 latest source/ R- shrinkTVPVAR_1.0.1.tar.gz 52.3 KiB
1.0.1 2026-04-26 source/ R- shrinkTVPVAR_1.0.1.tar.gz 52.3 KiB
1.0.1 2026-04-23 source/ R- shrinkTVPVAR_1.0.1.tar.gz 52.3 KiB
1.0.1 2026-04-09 windows/windows R-4.5 shrinkTVPVAR_1.0.1.zip 646.2 KiB
0.1.1 2025-04-20 source/ R- shrinkTVPVAR_0.1.1.tar.gz 41.0 KiB

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