Parsimony inducing priors for large scale state–space models

dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorMcCulloch, Robert E.
dc.contributor.authorTsay, Ruey S.
dc.creatorMcCulloch, Robert E.
dc.creatorTsay, Ruey S.
dc.date.accessioned2024-10-28T23:05:14Z
dc.date.available2024-10-28T23:05:14Z
dc.date.issued2022
dc.description.abstractState–space models are commonly used in the engineering, economic, and statistical literature. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state–space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state–space models. The proposed prior is a mixture of four commonly entertained models, yet achieving parsimony in high-dimensional systems. Here ‘‘parsimony’’ is represented by the idea that, in a large system, some states may not be time-varying. Our prior for the state–space component’s standard deviation is capable to accommodate different scenarios. Simulation and simple examples are used throughout this paper to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of the components of the S&P 100 index, leading to a state–space model with roughly five thousand time-varying states. Our model for this large system enables us to use parallel computing.en
dc.formatDigital
dc.format.extentp. 39 - 61
dc.identifier.doi10.1016/j.jeconom.2021.11.005
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/7189
dc.language.isoInglês
dc.relation.isboundProdução vinculada ao Núcleo de Ciências de Dados e Decisão
dc.relation.ispartofJournal of Econometrics
dc.subjectBayesian modelingen
dc.subjectConditional heteroscedasticityen
dc.subjectForward filtering and backward samplingen
dc.subjectParallel computingen
dc.subjectSparsityen
dc.subjectShrinkageen
dc.titleParsimony inducing priors for large scale state–space models
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S0304407621002621?via%3Dihub
local.publisher.countryNão Informado
local.typeArtigo Científico
publicationissue.issueNumber1
publicationvolume.volumeNumber230
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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