QBIC of SEM for diffusion processes from discrete observations

TL;DR

This paper introduces two quasi-Bayesian information criteria for structural equation modeling of diffusion processes from discrete data, demonstrating their consistency and evaluating their finite-sample performance through numerical experiments.

Contribution

It proposes novel model selection criteria for SEM with diffusion processes, establishing their theoretical consistency and assessing their practical effectiveness.

Findings

The criteria are shown to be model selection consistent.

Numerical experiments demonstrate good finite-sample performance.

The approach is based on asymptotic expansion of the marginal quasi-log likelihood.

Abstract

We deal with a model selection problem for structural equation modeling (SEM) with latent variables for diffusion processes. Based on the asymptotic expansion of the marginal quasi-log likelihood, we propose two types of quasi-Bayesian information criteria of the SEM. It is shown that the information criteria have model selection consistency. Furthermore, we examine the finite-sample performance of the proposed information criteria by numerical experiments.