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shrinkTVP (version 3.0.1)

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) .

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Version

Install

install.packages('shrinkTVP')

Monthly Downloads

328

Version

3.0.1

License

GPL (>= 2)

Maintainer

Peter Knaus

Last Published

February 18th, 2024

Functions in shrinkTVP (3.0.1)

simTVP

Generate synthetic data from a time-varying parameter model
updateTVP

One step update version of shrinkTVP with minimal overhead
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
print.shrinkTVP

Nicer printing of shrinkTVP objects
eval_pred_dens

Evaluate the one-step ahead predictive density of a fitted TVP model
plot.shrinkTVP

Graphical summary of posterior distribution
residuals.shrinkTVP

Calculate residuals for an estimated TVP model
LPDS

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

Calculate fitted historical values for an estimated TVP model
predict.shrinkTVP

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

Graphical summary of posterior distribution for a time-varying parameter
forecast_shrinkTVP

Draw from posterior predictive density of a fitted TVP model
plot.shrinkTVP_forc

Graphical summary of posterior predictive density