vector autoregressive models
Definition
Vector autoregressive models refers to statistical models that represent the dynamics of multiple variables over time, where each variable's current value depends on its own lagged values and the lagged values of other variables. In the context of intensive longitudinal data, multilevel vector autoregressive (VAR) models separate within-person effects (temporal dynamics within individuals across measurement occasions) from between-person effects (stable differences across individuals). Observed correlations between person-wise means are biased by within-person correlations.
Sources: Haslbeck & Epskamp (2024)
Related Terms
Applications
Vector Autoregressive Models and Person-wise Means
In multilevel vector autoregressive models, observed correlations between person-wise means—the average value of each variable for each individual across all measurement occasions—are influenced by within-person correlations. Correlations can appear between two traits at the between-person level even when no true between-person correlation exists, but only within-person correlations are present in the data.
Sources: Haslbeck & Epskamp (2024)
Vector Autoregressive Models and Within-person Correlations
Within-person correlations—the covariance structure of fluctuations around each individual's stable mean—directly influence the observed correlations between person-wise means in multilevel vector autoregressive models.
Sources: Haslbeck & Epskamp (2024)
