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Regime Switching State-Space Models Applied to Psychological Processes: Handling Missing Data and Making Inferences

Published online by Cambridge University Press:  01 January 2025

E. L. Hamaker  and
R. P. P. P. Grasman
E. L. Hamaker*
Affiliation:
Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University
R. P. P. P. Grasman
Affiliation:
Psychological Methods, University of Amsterdam
*
Requests for reprints should be sent to E.L. Hamaker, Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands. E-mail: e.l.hamaker@uu.nl
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Abstract

Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a “hot hand” and a “cold hand” in a top athlete. We model these processes with the regime switching state-space model proposed by Kim (J. Econom. 60:1–22, 1994), which results in both maximum likelihood estimates for the model parameters and estimates of the latent variables and the discrete states of the process. However, the current algorithm cannot handle missing data, which limits its applicability to psychological data. Moreover, the performance of standard errors for the purpose of making inferences about the parameter estimates is yet unknown. In this paper we modify Kim’s algorithm so it can handle missing data and we perform a simulation study to investigate its performance in (relatively) short time series in cases of different kinds of missing data and in case of complete data. Finally, we apply the regime switching state-space model to the three empirical data sets described above.

Keywords

Kalman filterregime switchingstate-space modelmissing data
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 2 , April 2012 , pp. 400 - 422
Copyright
Copyright © 2012 The Psychometric Society

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