Changing dynamics: Time-varying vector autoregressive models
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 (vector) 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 non-stationary processes that result from changing dynamics. In this project we present such a tool: the semi-parametric TV-VAR model. Notably, no prior knowledge of the processes that drive change in the dynamic structure is necessary. Thus, the TV-VAR model has significant potential for studying changing dynamics in psychology.
Researchers and partners
Behavioural and Social Sciences, Psychology
- dr. C.J. (Casper) Albers, Psychometrics and Statistics
University of Groningen, outside of Behavioural and Socials Sciences
- Marieke Wichers University Medical Center Groningen, Interdisciplinary Center for the Pathophysiology and Emotion regulation, Department of Psychiatry,
Partners outside of the University of Groningen
- Denny Borsboom University of Amsterdam
- Emilio Ferrer University of California, Davis
- Ellen L. Hamaker Utrecht University
- Jonas Haslbeck University of Amsterdam
- Francis Tuernlinckx KU Leuven
- Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (in press). Changing Dynamics: Time-varying autoregressive models using generalized additive modeling. Psychological Methods. doi:10.1037/met0000085.
- Bringmann, L., Ferrer, E., Hamaker, E., Borsboom, D., & Tuerlinckx, F. (2014). Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model. Multivariate Behavioral Research, 50(6), 730-731 (Conference proceeding).
University's focus areas
- Sustainable Society
|Last modified:||29 March 2021 10.18 a.m.|