Sparse Bayesian State-Space and Time-Varying Parameter Models

TL;DR

This paper reviews Bayesian variance selection methods for time-varying parameter models, introducing spike-and-slab priors and efficient MCMC estimation, with an application to US inflation data.

Contribution

It extends spike-and-slab shrinkage priors to TVP models using non-centered parametrization, enhancing variance selection in Bayesian time series analysis.

Findings

Effective variance selection via spike-and-slab priors

Improved MCMC estimation techniques for TVP models

Application demonstrates practical utility in US inflation modeling

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.