A time-varying threshold STAR model with applications

Autores
Dueker, Michael; Jackson, Laura E; Owyang, Michael T; Sola, Martin
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Smooth-transition autoregressive (STAR) models, competitors of Markov-switching models, are limited by an assumed time-invariant threshold level. We augment the STAR model with a time-varying threshold that can be interpreted as a ‘tipping level’ where the mean and dynamics of the VAR shift. Thus, the time-varying latent threshold level serves as a demarcation between regimes. We show how to estimate the model in a Bayesian framework using a Metropolis step and an unscented Kalman filter proposal. To show how allowing time variation in the threshold can affect the results, we present two applications: a model of the natural rate of unemployment and a model of regime-dependent government spending.
Fil: Dueker, Michael. Russell Investments; Estados Unidos
Fil: Jackson, Laura E. Bentley University; Estados Unidos
Fil: Owyang, Michael T. Federal Reserve Bank of St. Louis; Estados Unidos
Fil: Sola, Martin. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Regime switching
Smooth-transition autoregressive model
Nonlinear models
Unemployment
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/238459
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/238459
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A time-varying threshold STAR model with applicationsDueker, MichaelJackson, Laura EOwyang, Michael TSola, MartinRegime switchingSmooth-transition autoregressive modelNonlinear modelsUnemploymenthttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5Smooth-transition autoregressive (STAR) models, competitors of Markov-switching models, are limited by an assumed time-invariant threshold level. We augment the STAR model with a time-varying threshold that can be interpreted as a ‘tipping level’ where the mean and dynamics of the VAR shift. Thus, the time-varying latent threshold level serves as a demarcation between regimes. We show how to estimate the model in a Bayesian framework using a Metropolis step and an unscented Kalman filter proposal. To show how allowing time variation in the threshold can affect the results, we present two applications: a model of the natural rate of unemployment and a model of regime-dependent government spending.Fil: Dueker, Michael. Russell Investments; Estados UnidosFil: Jackson, Laura E. Bentley University; Estados UnidosFil: Owyang, Michael T. Federal Reserve Bank of St. Louis; Estados UnidosFil: Sola, Martin. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Press2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/238459Dueker, Michael; Jackson, Laura E; Owyang, Michael T; Sola, Martin; A time-varying threshold STAR model with applications; Oxford University Press; Oxford Open Economics; 2; 12-2022; 1-122752-5074CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/ooec/article/doi/10.1093/ooec/odac012/6887821info:eu-repo/semantics/altIdentifier/doi/10.1093/ooec/odac012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:54:04Zoai:ri.conicet.gov.ar:11336/238459instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:54:04.929CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A time-varying threshold STAR model with applications
title A time-varying threshold STAR model with applications
spellingShingle A time-varying threshold STAR model with applications
Dueker, Michael
Regime switching
Smooth-transition autoregressive model
Nonlinear models
Unemployment
title_short A time-varying threshold STAR model with applications
title_full A time-varying threshold STAR model with applications
title_fullStr A time-varying threshold STAR model with applications
title_full_unstemmed A time-varying threshold STAR model with applications
title_sort A time-varying threshold STAR model with applications
dc.creator.none.fl_str_mv Dueker, Michael
Jackson, Laura E
Owyang, Michael T
Sola, Martin
author Dueker, Michael
author_facet Dueker, Michael
Jackson, Laura E
Owyang, Michael T
Sola, Martin
author_role author
author2 Jackson, Laura E
Owyang, Michael T
Sola, Martin
author2_role author
author
author
dc.subject.none.fl_str_mv Regime switching
Smooth-transition autoregressive model
Nonlinear models
Unemployment
topic Regime switching
Smooth-transition autoregressive model
Nonlinear models
Unemployment
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Smooth-transition autoregressive (STAR) models, competitors of Markov-switching models, are limited by an assumed time-invariant threshold level. We augment the STAR model with a time-varying threshold that can be interpreted as a ‘tipping level’ where the mean and dynamics of the VAR shift. Thus, the time-varying latent threshold level serves as a demarcation between regimes. We show how to estimate the model in a Bayesian framework using a Metropolis step and an unscented Kalman filter proposal. To show how allowing time variation in the threshold can affect the results, we present two applications: a model of the natural rate of unemployment and a model of regime-dependent government spending.
Fil: Dueker, Michael. Russell Investments; Estados Unidos
Fil: Jackson, Laura E. Bentley University; Estados Unidos
Fil: Owyang, Michael T. Federal Reserve Bank of St. Louis; Estados Unidos
Fil: Sola, Martin. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Smooth-transition autoregressive (STAR) models, competitors of Markov-switching models, are limited by an assumed time-invariant threshold level. We augment the STAR model with a time-varying threshold that can be interpreted as a ‘tipping level’ where the mean and dynamics of the VAR shift. Thus, the time-varying latent threshold level serves as a demarcation between regimes. We show how to estimate the model in a Bayesian framework using a Metropolis step and an unscented Kalman filter proposal. To show how allowing time variation in the threshold can affect the results, we present two applications: a model of the natural rate of unemployment and a model of regime-dependent government spending.
publishDate 2022
dc.date.none.fl_str_mv 2022-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/238459
Dueker, Michael; Jackson, Laura E; Owyang, Michael T; Sola, Martin; A time-varying threshold STAR model with applications; Oxford University Press; Oxford Open Economics; 2; 12-2022; 1-12
2752-5074
CONICET Digital
CONICET
url http://hdl.handle.net/11336/238459
identifier_str_mv Dueker, Michael; Jackson, Laura E; Owyang, Michael T; Sola, Martin; A time-varying threshold STAR model with applications; Oxford University Press; Oxford Open Economics; 2; 12-2022; 1-12
2752-5074
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/ooec/article/doi/10.1093/ooec/odac012/6887821
info:eu-repo/semantics/altIdentifier/doi/10.1093/ooec/odac012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.976206

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