22.01.2026
New article on frequentist forecasting in intensive longitudinal data
We introduce a first frequentist filter for regime-switching state-space (RSSS) models which allows hidden Markov(-switching) models to depend on both latent within- and between-individual characteristics. In an empirical study, the filter is applied to forecast emotions and behavior related to student dropout in math. Parameter recovery and prediction of regime and dynamic latent variables are evaluated through simulation study.
Okuyama, K., Schaffland, T. F., Kilian, P., Brandt, H., & Kelava, A. (2025). Frequentist forecasting in regime-switching models with extended Hamilton filter. PsyArXiv preprint. https://doi.org/10.48550/arXiv.2512.18149 (manuscript under review)
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