Bayesian analysis of panel data using an MTAR model

Yoon Young Jung, Dong Wan Shin, Man Suk Oh

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.

Original languageEnglish
Pages (from-to)841-854
Number of pages14
JournalJournal of Applied Statistics
Issue number8
StatePublished - Oct 2005


  • MCMC
  • Model selection
  • MTAR
  • Panel data


Dive into the research topics of 'Bayesian analysis of panel data using an MTAR model'. Together they form a unique fingerprint.

Cite this