In emotion dynamic research, one distinguishes various elementary emotion dynamic features, which are studied using intensive longitudinal data. Typically, each emotion dynamic feature is quantified separately, which hampers the study of relationships between various features. Further, the length of the observed time series in emotion research is limited and often suffers from a high percentage of missing values. In this article, we propose a vector autoregressive Bayesian dynamic model that is useful for emotion dynamic research. The model encompasses 6 elementary properties of emotions and can be applied with relatively short time series, including missing data. The individual elementary properties covered are within-person variability, innovation variability, inertia, granularity, cross-lag regression, and average intensity. The model can be applied to both univariate and multivariate time series, allowing one to model the relationships between emotions. One may include external variables and non-Gaussian observed data. We illustrate the usefulness of the model on data involving 50 participants self-reporting on their experience of 3 emotions across the period of 1 week using experience sampling.
Krone et al. REF proposed a vector autoregressive Bayesian sentiment dynamics model to predict the basic emotions with long-term dependence based on short time sequences.
A Multivariate Statistical Model for Emotion Dynamics
Emotion
Psychology Medicine