Package: shrinkTVP 3.0.1

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, both dynamic and static. 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> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.

Authors:Peter Knaus [aut, cre], Angela Bitto-Nemling [aut], Annalisa Cadonna [aut], Sylvia Frühwirth-Schnatter [aut], Daniel Winkler [ctb], Kemal Dingic [ctb]

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7 exports 2 stars 0.82 score 9 dependencies 1 dependents 6 scripts 437 downloads

Last updated 7 months agofrom:9e254834d8. Checks:ERROR: 1 NOTE: 2 OK: 6. Indexed: yes.

Exports:eval_pred_densforecast_shrinkTVPLPDSshrinkDTVPshrinkTVPsimTVPupdateTVP

Dependencies:codaGIGrvglatticeRcppRcppArmadilloRcppGSLRcppProgressstochvolzoo