Efficient Bayesian Estimation of Dynamic Structural Equation Models via State Space Marginalization
This paper shows that the within-level part of any dynamic structural equation model can be reformulated as a linear Gaussian state space model. Consequently, the latent states can be analytically marginalized using a Kalman filter, allowing for highly efficient estimation via Hamiltonian Monte Carlo. This makes DSEM estimation computationally tractable for much larger datasets than what has been previously possible. arXiv preprint.