A Bayesian inference for time series via copula-based Markov chain models

Li Hsien Sun, Chang Shang Lee, Takeshi Emura

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper studies the nonstandardized Student’s t-distribution for fitting serially correlated observations where serial dependence is described by the copula-based Markov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide the simulations to examine the performance and also analyze the stock price data in empirical studies for illustration.

Original languageEnglish
Pages (from-to)2897-2913
Number of pages17
JournalCommunications in Statistics - Simulation and Computation
Issue number11
StatePublished - 2020


  • Bayesian inference
  • Clayton copula
  • Markov chain Monte Carlo
  • Metropolis-Hastings algorithm
  • Nonstandardized Student’s t-distribution


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