  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.
