Quotation Bitto-Nemling, Angela, Frühwirth-Schnatter, Sylvia. 2018. Time Varying Parameter Mixture Model. ISBA 2018, Edinburgh, United Kingdom, 24.06.-29.06.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Poster presented at an academic conference or symposium
Language English
Title Time Varying Parameter Mixture Model
Event ISBA 2018
Date 24.06.-29.06.
Location Edinburgh
Country United Kingdom
Year 2018
URL https://bayesian.org/isba2018/

Associations

People
Bitto-Nemling, Angela (Former researcher)
Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
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