论文标题
稀疏的贝叶斯州空间和时变参数模型
Sparse Bayesian State-Space and Time-Varying Parameter Models
论文作者
论文摘要
在本章中，我们回顾了贝叶斯框架中单变量和多变量时间序列的时变参数（TVP）模型的差异选择。我们展示了如何通过使用非中心的参数化将连续和离散的尖峰和斜线收缩率都从回归模型的变量选择转移到TVP模型的方差选择。我们讨论有效的MCMC估计，并为美国通货膨胀建模提供了应用。
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.
