A Bayesian analysis on time series structural equation models

Yi Fu Wang, Tsai Hung Fan

Research output: Contribution to journalArticlepeer-review


Structural equation models (SEM) have been extensively used in behavioral, social, and psychological research to model relations between the latent variables and the observations. Most software packages for the fitting of SEM rely on frequentist methods. Traditional models and software are not appropriate for analysis of the dependent observations such as time-series data. In this study, a structural equation model with a time series feature is introduced. A Bayesian approach is used to solve the model with the aid of the Markov chain Monte Carlo method. Bayesian inferences as well as prediction with the proposed time series structural equation model can also reveal certain unobserved relationships among the observations. The approach is successfully employed using real Asian, American and European stock return data.

Original languageEnglish
Pages (from-to)2071-2078
Number of pages8
JournalJournal of Statistical Planning and Inference
Issue number6
Publication statusPublished - 2011 Jun

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


Dive into the research topics of 'A Bayesian analysis on time series structural equation models'. Together they form a unique fingerprint.

Cite this