Sparse Bayesian State-Space and Time-Varying Parameter Models

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.

Also appears as a chapter in the Handbook of Bayesian Variable Selection (2021), edited by Mahlet G. Tadesse and Marina Vannucci