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shrinkTVP (version 3.1.0)

Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage

Description

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) and Cadonna et al. (2020) and Knaus and Frühwirth-Schnatter (2023) . For details on the package, please see Knaus et al. (2021) . For the multivariate extension, see the 'shrinkTVPVAR' package.

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Version

Install

install.packages('shrinkTVP')

Monthly Downloads

328

Version

3.1.0

License

GPL (>= 2)

Maintainer

Peter Knaus

Last Published

June 2nd, 2025

Functions in shrinkTVP (3.1.0)

updateTVP

One step update version of shrinkTVP with minimal overhead
simTVP

Generate synthetic data from a time-varying parameter model
shrinkTVP

Markov Chain Monte Carlo (MCMC) for time-varying parameter models with shrinkage
shrinkDTVP

Markov Chain Monte Carlo (MCMC) for time-varying parameter models with dynamic shrinkage
fitted.shrinkTVP

Calculate fitted historical values for an estimated TVP model
plot.mcmc.tvp

Graphical summary of posterior distribution for a time-varying parameter
plot.shrinkTVP

Graphical summary of posterior distribution
plot.shrinkTVP_forc

Graphical summary of posterior predictive density
LPDS

Calculate the Log Predictive Density Score for a fitted TVP model
residuals.shrinkTVP

Calculate residuals for an estimated TVP model
eval_pred_dens

Evaluate the one-step ahead predictive density of a fitted TVP model
forecast_shrinkTVP

Draw from posterior predictive density of a fitted TVP model
predict.shrinkTVP

Calculate predicted historical values for an estimated TVP model
print.shrinkTVP

Nicer printing of shrinkTVP objects