Time Varying Parameter Mixture Model

Angela Bitto-Nemling, Sylvia Frühwirth-Schnatter

Publikation: KonferenzbeitragKonferenzposter

Abstract

We introduce the TVP (Time Varying Parameter) Mixture Model. Based on previous work (Bitto and Frühwirth-Schnatter, 2017), the focus of this paper is the estimation of a time-varying parameter model with shrinkage priors. The key idea is the usage of spike-and-slab priors for the process variances. We assume that both spike and slab have a hierarchical representation as a normal-gamma prior (Griffin and Brown,2010). In this way we extend previous work based on spike-and-slab priors (Frühwirth-Schnatter and Wagner, 2010) and Bayesian Lasso type priors (Belmonte et al. 2014). We present necessary modifications of our efficient MCMC estimation scheme, exploiting ideas such as ancillarity-sufficiency interweaving (Yu and Meng, 2011). We present our idea with a simulation study.
OriginalspracheEnglisch
PublikationsstatusVeröffentlicht - 2018

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