Akaike-type information criterion of SEM for jump-diffusion processes based on high-frequency data

TL;DR

This paper develops an Akaike-type information criterion tailored for SEM applied to jump-diffusion processes, facilitating model selection using high-frequency data with jumps, supported by simulation results.

Contribution

It introduces a novel information criterion for SEM that accounts for jump-diffusion processes and high-frequency data, advancing model selection methods in this context.

Findings

The proposed criterion performs well in finite samples.

Simulation studies demonstrate its effectiveness.

It enables accurate model selection with jump data.

Abstract

Structural equation modeling (SEM) is a statistical method used to investigate relationships among latent variables. In SEM, the model must be specified in advance. However, in practice, statisticians often have several candidate models and need to select the most appropriate one. Consequently, model selection is a key issue in SEM, and information criteria are commonly used to address this issue. In this study, we develop an Akaike-type information criterion of SEM for jump-diffusion processes, which enables model selection for SEM based on high-frequency data with jumps. Simulation studies are conducted to illustrate the finite-sample performance of the proposed method.