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Efficient simulation and integrated likelihood estimation in state space models

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dc.contributor.authorChan, Chi Chun (Joshua)
dc.contributor.authorJeliazkov, Ivan
dc.date.accessioned2015-12-08T22:23:04Z
dc.date.issued2009
dc.date.updated2016-02-24T12:00:52Z
dc.description.abstractWe consider the problem of implementing simple and efficient Markov chain Monte Carlo (MCMC) estimation algorithms for state space models. A conceptually transparent derivation of the posterior distribution of the states is discussed, which also leads to
dc.identifier.issn2040-3607
dc.identifier.urihttp://hdl.handle.net/1885/32715
dc.publisherInderscience Publishers
dc.sourceInternational Journal of Mathematical Modelling and Numerical Optimisation
dc.subjectKeywords: Banded matrix; Bayesian estimation; Collapsed sampler; Dynamic factor model; Kalman filter; Markov chain Monte Carlo; MCMC; State smoothing; Time-varying parameter model
dc.titleEfficient simulation and integrated likelihood estimation in state space models
dc.typeJournal article
local.bibliographicCitation.issue1/2
local.bibliographicCitation.lastpage120
local.bibliographicCitation.startpage101
local.contributor.affiliationChan, Chi Chun (Joshua), College of Business and Economics, ANU
local.contributor.affiliationJeliazkov, Ivan, University of California
local.contributor.authoruidChan, Chi Chun (Joshua), u4935553
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor140305 - Time-Series Analysis
local.identifier.absseo910199 - Macroeconomics not elsewhere classified
local.identifier.ariespublicationU9501697xPUB94
local.identifier.citationvolume1
local.identifier.doi10.1504/IJMMNO.2009.030090
local.identifier.scopusID2-s2.0-77956292458
local.type.statusPublished Version

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