From RI-CLPM to panelGVAR: Evaluating Stationarity in Network Modeling of Panel Data

The random-intercept cross-lagged panel model (RI-CLPM) and the panel graphical vector-autoregression model (panelGVAR) are two popular frameworks for multivariate panel data. These models differ along two dimensions, and we contribute new methodology on both. The first dimension is stationarity: the panelGVAR constrains all parameters to be equal over time and treats the first wave as endogenous, whereas the RI-CLPM, as commonly specified, leaves every parameter wave-specific. Our first contribution is a pipeline of nested models: a guarded decision tree that tests one stationarity constraint at a time and releases those that fail. The second dimension is the parameterization: our second contribution integrates the GGMs into the RI-CLPM, yielding the random-intercept cross-lagged panel network model (RI-CLPN), which standard SEM software cannot specify. Both are implemented in the R package psychonetrics. Two simulation studies evaluate theper-constraint verdicts under likelihood-ratio, AIC, and BIC criteria and network recovery under mean and variance non-stationarity. They show which violations the pipeline detects, and what ignoring them costs when a panelGVAR is fitted directly. A tutorial and two empirical examples illustrate the pipeline.