Quotation Bitto-Nemling, Angela, Cadonna, Annalisa, Frühwirth-Schnatter, Sylvia, Knaus, Peter. 2019. Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP.


RIS


BibTeX

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 Frühwirth-Schnatter 2019), which provides a fully Bayesian implementation of shrinkage priors for TVP models, taking advantage of recent developments in the literature, in particular that of Bitto and Frühwirth-Schnatter (2019). The package shrinkTVP allows for posterior simulation of the parameters through an efficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary and visualization methods, as well as the possibility of assessing predictive performance through log predictive density scores (LPDSs), 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.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Year 2019
URL https://arxiv.org/abs/1907.07065

Associations

People
Cadonna, Annalisa (Former researcher)
Frühwirth-Schnatter, Sylvia (Details)
Knaus, Peter (Details)
External
Bitto-Nemling, Angela (NA, Austria)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search