Sparse Bayesian State-Space and Time-Varying Parameter Models

Sylvia Fr\"uhwirth-Schnatter and Peter Knaus

Papers from arXiv.org

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.

Date: 2022-07
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in Handbook of Bayesian Variable Selection (2021): 297-326

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.12147

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