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

Abstract: 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 assum...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Bringmann, Laura F., Vigo, Daniel Eduardo, Borsboom, Denny, Hamaker, Ellen L., Aubert, André E., Tuerlinckx, Francis
Formato: Artículo
Lenguaje:Inglés
Publicado: American Psychological Association 2020
Materias:
SERIES TEMPORALES
METODOS ESTADISTICOS
REGRESION LINEAL
PSICOLOGIA
MODELOS MATEMATICOS
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/10326
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139123456789-10326
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic SERIES TEMPORALES
METODOS ESTADISTICOS
REGRESION LINEAL
PSICOLOGIA
MODELOS MATEMATICOS
spellingShingle SERIES TEMPORALES
METODOS ESTADISTICOS
REGRESION LINEAL
PSICOLOGIA
MODELOS MATEMATICOS
Bringmann, Laura F.
Vigo, Daniel Eduardo
Borsboom, Denny
Hamaker, Ellen L.
Aubert, André E.
Tuerlinckx, Francis
Changing dynamics : time-varying autoregressive models using generalized additive modeling
topic_facet SERIES TEMPORALES
METODOS ESTADISTICOS
REGRESION LINEAL
PSICOLOGIA
MODELOS MATEMATICOS
description Abstract: 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.
format Artículo
author Bringmann, Laura F.
Vigo, Daniel Eduardo
Borsboom, Denny
Hamaker, Ellen L.
Aubert, André E.
Tuerlinckx, Francis
author_facet Bringmann, Laura F.
Vigo, Daniel Eduardo
Borsboom, Denny
Hamaker, Ellen L.
Aubert, André E.
Tuerlinckx, Francis
author_sort Bringmann, Laura F.
title Changing dynamics : time-varying autoregressive models using generalized additive modeling
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
publisher American Psychological Association
publishDate 2020
url https://repositorio.uca.edu.ar/handle/123456789/10326
work_keys_str_mv AT bringmannlauraf changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
AT vigodanieleduardo changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
AT borsboomdenny changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
AT hamakerellenl changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
AT aubertandree changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
AT tuerlinckxfrancis changingdynamicstimevaryingautoregressivemodelsusinggeneralizedadditivemodeling
bdutipo_str Repositorios
_version_ 1764820524282675201

Ejemplares similares