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shrinkTVPVAR (version 0.1.1)

Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage

Description

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with shrinkage priors. Details on the algorithms used are provided in Cadonna et al. (2020) and Knaus et al. (2021) .

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Version

Install

install.packages('shrinkTVPVAR')

Monthly Downloads

147

Version

0.1.1

License

GPL (>= 2)

Maintainer

Peter Knaus

Last Published

September 16th, 2024

Functions in shrinkTVPVAR (0.1.1)

forecast_shrinkTVPVAR

Draw from posterior predictive density of a fitted TVP-VAR-SV model
plot.shrinkTVPVAR_fit

Graphical summary of posterior distribution of fitted values for TVP-VAR model
plot.shrinkTVPVAR

Plotting method for shrinkTVPVAR objects
plot.mcmc.var

Plotting method for mcmc.var objects
TV_heatmap

Heatmap of hyperparameters of time-varying coefficient matrix in a TVP-VAR model
fitted.shrinkTVPVAR

Calculate fitted historical values for an estimated TVP-VAR-SV model
density_plotter

Kernel density plots of posterior distribution for hyperparameters of time-varying coefficient matrix in a TVP-VAR model
gen_TVP_params

Generate TVP_params that can be used as input for a TVP-VAR-SV model
plot.mcmc.tvp.var

Plotting method for mcmc.tvp.var objects
plot.shrinkTVPVAR_forc

Graphical summary of posterior predictive density for TVP-VAR-SV model
simTVPVAR

Generate synthetic data from a TVP-VAR-SV model
print.shrinkTVPVAR

Nicer printing of shrinkTVPVAR objects
state_plotter

Graphical summary of posterior distribution for a time-varying coefficient matrix in a TVP-VAR model
shrinkTVPVAR

Markov Chain Monte Carlo (MCMC) for TVP-VAR-SV models with shrinkage