Shrinkage in the Time-Varying Parameter Model Framework Using the <i>R</i> Package <b>shrinkTVP</b>

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting TVP is well known. This issue can be dealt using appropriate global-local shrinkage priors, pull time-varying parameters towards static ones. In this paper, we introduce R package shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and FrühwirthSchnatter 2021), provides a fully Bayesian implementation priors for models, taking advantage recent developments literature, particular those Bitto Frühwirth-Schnatter (2019) Frühwirth-Schnatter, Knaus (2020). The allows posterior simulation through an efficient Markov Chain Monte Carlo scheme. Moreover, summary visualization methods, as possibility assessing predictive performance log-predictive density scores, provided. computationally intensive tasks have been implemented C++ interfaced R. paper includes brief overview package. Furthermore, core functionalities illustrated, both simulated real data.