shrinkTVP

Peter Knaus; Angela Bitto-Nemling; Annalisa Cadonna; Sylvia Frühwirth-Schnatter

shrinkTVP

Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter

Institute for Statistics and Mathematics

Publication: Non-textual form › Software

Abstract

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>.

Original language	English
Publication status	Published - 13 May 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

102022 Software development
502025 Econometrics
101018 Statistics

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Cite this

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title = "shrinkTVP",

abstract = "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 Fr{\"u}hwirth-Schnatter (2019) .",

author = "Peter Knaus and Angela Bitto-Nemling and Annalisa Cadonna and Sylvia Fr{\"u}hwirth-Schnatter",

year = "2021",

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AU - Knaus, Peter

AU - Bitto-Nemling, Angela

AU - Cadonna, Annalisa

AU - Frühwirth-Schnatter, Sylvia

PY - 2021/5/13

Y1 - 2021/5/13

N2 - 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) .

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