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Time-varying parameter (TVP) models are widely used in time series analysisto flexibly deal with processes which gradually change over time. However, therisk of overfitting in TVP models is well known. This issue can be dealt withusing appropriate global-local shrinkage priors, which pull time-varyingparameters towards static ones. In this paper, we introduce the R packageshrinkTVP (Knaus, Bitto-Nemling, Cadonna, and Fr\"uhwirth-Schnatter 2019),which provides a fully Bayesian implementation of shrinkage priors for TVPmodels, taking advantage of recent developments in the literature, inparticular that of Bitto and Fr\"uhwirth-Schnatter (2019). The packageshrinkTVP allows for posterior simulation of the parameters through anefficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary andvisualization methods, as well as the possibility of assessing predictiveperformance through log predictive density scores (LPDSs), are provided. Thecomputationally intensive tasks have been implemented in C++ and interfacedwith R. The paper includes a brief overview of the models and shrinkage priorsimplemented in the package. Furthermore, core functionalities are illustrated,both with simulated and real data.

Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP