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shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.
Getting started
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Package details |
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| Author | Peter Knaus [aut, cre] (<https://orcid.org/0000-0001-6498-7084>), Angela Bitto-Nemling [aut], Annalisa Cadonna [aut] (<https://orcid.org/0000-0003-0360-7628>), Sylvia Frühwirth-Schnatter [aut] (<https://orcid.org/0000-0003-0516-5552>), Daniel Winkler [ctb], Kemal Dingic [ctb] |
| Maintainer | Peter Knaus <peter.knaus@wu.ac.at> |
| License | GPL (>= 2) |
| Version | 2.1.1 |
| Package repository | View on CRAN |
| Installation |
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