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

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

Bayesian inference, Gibbs sampler, Markov chain Monte Carlo (MCMC), normalgamma prior, time-varying parameter (TVP) models, log-predictive density scores.

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

Peter Knaus

https://www.jstatsoft.org/article/view/v100i13

WU Vienna

November 2021

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