Changing dynamics: Time-varying autoregressive models using generalized additive modeling

Autores
Bringmann, Laura F.; Hamaker, Ellen L.; Vigo, Daniel Eduardo; Aubert, André; Borsboom, Denny; Tuerlinckx, Francis
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.
Fil: Bringmann, Laura F.. Katholikie Universiteit Leuven; Bélgica
Fil: Hamaker, Ellen L.. University of Utrecht; Países Bajos
Fil: Vigo, Daniel Eduardo. Pontificia Universidad Católica Argentina ; Argentina
Fil: Aubert, André. Katholikie Universiteit Leuven; Bélgica
Fil: Borsboom, Denny. University of Amsterdam; Países Bajos
Fil: Tuerlinckx, Francis. Katholikie Universiteit Leuven; Bélgica
Materia
Time Series
Nonstationarity
Autoregressive Models
Generalized Additive Models
Splines
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/39349

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network_name_str CONICET Digital (CONICET)
spelling Changing dynamics: Time-varying autoregressive models using generalized additive modelingBringmann, Laura F.Hamaker, Ellen L.Vigo, Daniel EduardoAubert, AndréBorsboom, DennyTuerlinckx, FrancisTime SeriesNonstationarityAutoregressive ModelsGeneralized Additive ModelsSplineshttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.Fil: Bringmann, Laura F.. Katholikie Universiteit Leuven; BélgicaFil: Hamaker, Ellen L.. University of Utrecht; Países BajosFil: Vigo, Daniel Eduardo. Pontificia Universidad Católica Argentina ; ArgentinaFil: Aubert, André. Katholikie Universiteit Leuven; BélgicaFil: Borsboom, Denny. University of Amsterdam; Países BajosFil: Tuerlinckx, Francis. Katholikie Universiteit Leuven; BélgicaAmerican Psychological Association2017-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/39349Bringmann, Laura F.; Hamaker, Ellen L.; Vigo, Daniel Eduardo; Aubert, André; Borsboom, Denny; et al.; Changing dynamics: Time-varying autoregressive models using generalized additive modeling; American Psychological Association; Psychological Methods; 22; 3; 9-2017; 409-4251082-989X1939-1463CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1037/met0000085info:eu-repo/semantics/altIdentifier/url/http://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000085info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:25Zoai:ri.conicet.gov.ar:11336/39349instacron: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:34982025-09-03 09:53:26.021CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Changing dynamics: Time-varying autoregressive models using generalized additive modeling
title Changing dynamics: Time-varying autoregressive models using generalized additive modeling
spellingShingle Changing dynamics: Time-varying autoregressive models using generalized additive modeling
Bringmann, Laura F.
Time Series
Nonstationarity
Autoregressive Models
Generalized Additive Models
Splines
title_short Changing dynamics: Time-varying autoregressive models using generalized additive modeling
title_full Changing dynamics: Time-varying autoregressive models using generalized additive modeling
title_fullStr Changing dynamics: Time-varying autoregressive models using generalized additive modeling
title_full_unstemmed Changing dynamics: Time-varying autoregressive models using generalized additive modeling
title_sort Changing dynamics: Time-varying autoregressive models using generalized additive modeling
dc.creator.none.fl_str_mv Bringmann, Laura F.
Hamaker, Ellen L.
Vigo, Daniel Eduardo
Aubert, André
Borsboom, Denny
Tuerlinckx, Francis
author Bringmann, Laura F.
author_facet Bringmann, Laura F.
Hamaker, Ellen L.
Vigo, Daniel Eduardo
Aubert, André
Borsboom, Denny
Tuerlinckx, Francis
author_role author
author2 Hamaker, Ellen L.
Vigo, Daniel Eduardo
Aubert, André
Borsboom, Denny
Tuerlinckx, Francis
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Time Series
Nonstationarity
Autoregressive Models
Generalized Additive Models
Splines
topic Time Series
Nonstationarity
Autoregressive Models
Generalized Additive Models
Splines
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.
Fil: Bringmann, Laura F.. Katholikie Universiteit Leuven; Bélgica
Fil: Hamaker, Ellen L.. University of Utrecht; Países Bajos
Fil: Vigo, Daniel Eduardo. Pontificia Universidad Católica Argentina ; Argentina
Fil: Aubert, André. Katholikie Universiteit Leuven; Bélgica
Fil: Borsboom, Denny. University of Amsterdam; Países Bajos
Fil: Tuerlinckx, Francis. Katholikie Universiteit Leuven; Bélgica
description In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the temporal dependency of a process) govern the time series. Often a change in the process, such as emotional well-being during therapy, is the very reason why it is interesting and important to study psychological dynamics. As a result, there is a need for an easily applicable method for studying such nonstationary processes that result from changing dynamics. In this article we present such a tool: the semiparametric TV-AR model. We show with a simulation study and an empirical application that the TV-AR model can approximate nonstationary processes well if there are at least 100 time points available and no unknown abrupt changes in the data. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. We conclude that the TV-AR model has significant potential for studying changing dynamics in psychology.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
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/11336/39349
Bringmann, Laura F.; Hamaker, Ellen L.; Vigo, Daniel Eduardo; Aubert, André; Borsboom, Denny; et al.; Changing dynamics: Time-varying autoregressive models using generalized additive modeling; American Psychological Association; Psychological Methods; 22; 3; 9-2017; 409-425
1082-989X
1939-1463
CONICET Digital
CONICET
url http://hdl.handle.net/11336/39349
identifier_str_mv Bringmann, Laura F.; Hamaker, Ellen L.; Vigo, Daniel Eduardo; Aubert, André; Borsboom, Denny; et al.; Changing dynamics: Time-varying autoregressive models using generalized additive modeling; American Psychological Association; Psychological Methods; 22; 3; 9-2017; 409-425
1082-989X
1939-1463
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1037/met0000085
info:eu-repo/semantics/altIdentifier/url/http://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000085
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Psychological Association
publisher.none.fl_str_mv American Psychological Association
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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