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

• Autores: Bringmann, Laura F.; Vigo, Daniel Eduardo; Borsboom, Denny; Hamaker, Ellen L.; Aubert, André E.; Tuerlinckx, Francis
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Psychological Methods. 2017, 22 (3)
• Materia: SERIES TEMPORALES; METODOS ESTADISTICOS; REGRESION LINEAL; PSICOLOGIA; MODELOS MATEMATICOS
• Institución: Pontificia Universidad Católica Argentina

Fil: Bringmann, Laura F. University of Leuven. Department Quantitative Psychology and Individual Differences; Bélgica
Fil: Hamaker, Ellen L. University of Utrecht; Países Bajos
Fil: Vigo, Daniel Eduardo. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas; Argentina
Fil: Vigo, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Vigo, Daniel Eduardo. University of Leuven; Bélgica
Fil: Aubert, André E. University of Leuven; Bélgica
Fil: Borsboom, Denny. University of Amsterdam; Países Bajos
Fil: Tuerlinckx, Francis. University of Leuven; Bélgica
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.