Grothe, Oliver, Kleppe, Tore Selland and Liesenfeld, Roman (2019). The Gibbs sampler with particle efficient importance sampling for state-space models*. Econom. Rev., 38 (10). S. 1152 - 1176. PHILADELPHIA: TAYLOR & FRANCIS INC. ISSN 1532-4168

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Abstract

We consider Particle Gibbs (PG) for Bayesian analysis of non-linear non-Gaussian state-space models. As a Monte Carlo (MC) approximation of the Gibbs procedure, PG uses sequential MC (SMC) importance sampling inside the Gibbs to update the latent states. We propose to combine PG with the Particle Efficient Importance Sampling (PEIS). By using SMC sampling densities which are approximately globally fully adapted to the targeted density of the states, PEIS can substantially improve the simulation efficiency of the PG relative to existing PG implementations. The efficiency gains are illustrated in PG applications to a non-linear local-level model and stochastic volatility models.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Grothe, OliverUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kleppe, Tore SellandUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Liesenfeld, RomanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-127152
DOI: 10.1080/07474938.2018.1536098
Journal or Publication Title: Econom. Rev.
Volume: 38
Number: 10
Page Range: S. 1152 - 1176
Date: 2019
Publisher: TAYLOR & FRANCIS INC
Place of Publication: PHILADELPHIA
ISSN: 1532-4168
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
INTEREST-RATES; SIMULATION; INFERENCEMultiple languages
Economics; Mathematics, Interdisciplinary Applications; Social Sciences, Mathematical Methods; Statistics & ProbabilityMultiple languages
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/12715

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