Abstract
In this paper 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 and a real world application.
(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 and a real world application.
| Originalsprache | Englisch |
|---|---|
| Publikationsstatus | Veröffentlicht - 2017 |