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) and Cadonna et al. (2020) .


Reference manual

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2.0.2 by Peter Knaus, 5 months ago

Browse source code at https://github.com/cran/shrinkTVP

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

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, GIGrvg, stochvol, coda, methods, utils, zoo

Suggests testthat, knitr, rmarkdown, R.rsp

Linking to Rcpp, RcppArmadillo, GIGrvg, RcppProgress, stochvol

Imported by shrinkDSM.

See at CRAN