Causal inference for panel data with dynamic multivariate panel models – Jouni Helske (INVEST)

Longitudinal data consisting of various measurements from multiple subjects followed over several time points, are commonly studied in social sciences and other fields. Such data can naturally be analyzed in various ways, depending on the research questions and the characteristics of the data. Popular, somewhat overlapping modelling approaches include fixed effect models, variations of cross-lagged panel models, and dynamic structural equation models. In this talk, I present the dynamic multivariate panel model (DMPM), a flexible Bayesian approach that extends many existing modelling approaches. The DMPM supports ordinary time-invariant effects, individual-level random effects as well as effects varying smoothly in time. Moreover, it can handle multiple simultaneous responses across a wide variety of mixed distributions and allows estimating long-term causal effects of interventions.