Sparse Bayesian State-Space and Time-Varying Parameter Models

• Tác giả: Sylvia Frühwirth-Schnatter, Peter Knaus
• Ngôn ngữ: eng
• Ký hiệu phân loại: 511.8 Mathematical models (Mathematical simulation)
• Thông tin xuất bản: 2022
• Bộ sưu tập: Metadata
• ID: 195576

Tóm tắt

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