QBIC of SEM for jump-diffusion processes based on high-frequency data

TL;DR

This paper introduces a quasi-Bayesian information criterion (QBIC) for structural equation modeling (SEM) applied to jump-diffusion processes, ensuring consistent model selection with high-frequency data.

Contribution

The paper proposes a novel QBIC tailored for SEM in jump-diffusion processes, demonstrating its model-selection consistency.

Findings

The QBIC achieves consistent model selection in SEM for jump-diffusion processes.

The proposed criterion is applicable to high-frequency data analysis.

Theoretical proof of model-selection consistency is provided.

Abstract

Structural equation modeling (SEM) is a statistical method for analyzing relationships among latent variables. Since SEM is a confirmatory method, the model needs to be specified in advance. In practice, however, statisticians have several candidate models and aim to select the most appropriate one among them. In this paper, we consider model selection in SEM for jump-diffusion processes. We propose a quasi-Bayesian information criterion (QBIC) for the SEM and show that the proposed criterion has model-selection consistency.