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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/39349
Ver los metadatos del registro completo
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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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1842269224921202688 |
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13.13397 |