Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
Evaluate the one-step ahead predictive density of a fitted TVP model
Calculate fitted historical values for an estimated TVP model
Draw from posterior predictive density of a fitted TVP model
Calculate the Log Predictive Density Score for a fitted TVP model
Graphical summary of posterior distribution for a time-varying paramet...
Graphical summary of posterior distribution
Graphical summary of posterior predictive density
Calculate predicted historical values for an estimated TVP model
Nicer printing of shrinkTVP objects
Calculate residuals for an estimated TVP model
Markov Chain Monte Carlo (MCMC) for time-varying parameter models with...
Markov Chain Monte Carlo (MCMC) for time-varying parameter models with...
Generate synthetic data from a time-varying parameter model
One step update version of shrinkTVP with minimal overhead
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) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.