Abstract
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.
Highlights
Time-varying parameter (TVP) models are widely used in time series analysis, because of their flexibility and ability to capture gradual changes in the model parameters over time.shrinkTVP: Shrinkage in the Time-Varying Parameter Model Framework in RThe popularity of TVP models in macroeconomics and finance is based on the fact that, in most applications, the influence of certain predictors on the outcome variables varies over time (Primiceri 2005; Dangl and Halling 2012; Belmonte, Koop, and Korobolis 2014)
A key contribution in this direction was the introduction of the non-centered parameterization of TVP models in Frühwirth-Schnatter and Wagner (2010), which recasts the problem of variance selection and shrinkage in terms of variable selection, allowing any tool used to this end in multiple regression models to be used to perform selection or shrinkage of variances
The goal of this paper was to introduce the reader to the functionality of the R package shrinkTVP (Knaus et al 2021)
Summary
Time-varying parameter (TVP) models are widely used in time series analysis, because of their flexibility and ability to capture gradual changes in the model parameters over time. The package shrinkTVP is designed to provide an easy entry point for fitting TVP models with shrinkage priors, while giving more experienced users the option to adapt the model to their needs. The package includes the macroeconomic data set analysed in (Primiceri 2005) as example data set, usmacro.update, which we use in our predictive exercise in Section 5 to showcast the effect of introducing shrinkage priors on time-varying parameters. The R package bsts (Scott 2021) performs Bayesian analysis for structural time series models, a highly relevant class of state space models including DLMs. bsts is a very powerful package that allows shrinkage for static regression coefficients using spike and slab priors.
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