Sparse Bayesian State-Space and Time-Varying Parameter Models
Econometrics
2022-07-26 v1 Methodology
Abstract
In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using a non-centered parametrization. We discuss efficient MCMC estimation and provide an application to US inflation modeling.
Keywords
Cite
@article{arxiv.2207.12147,
title = {Sparse Bayesian State-Space and Time-Varying Parameter Models},
author = {Sylvia Frühwirth-Schnatter and Peter Knaus},
journal= {arXiv preprint arXiv:2207.12147},
year = {2022}
}
Comments
Also appears as a chapter in the Handbook of Bayesian Variable Selection (2021), edited by Mahlet G. Tadesse and Marina Vannucci