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>.

Getting started

Package details

AuthorPeter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>)
MaintainerPeter Knaus <peter.knaus@wu.ac.at>
LicenseGPL (>= 2)
Version1.0.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("shrinkTVPVAR")

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shrinkTVPVAR documentation built on June 8, 2025, 10:39 a.m.