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Bayesian analysis of panel data using an MTAR model

Author

Listed:
  • Yoon Young Jung
  • Dong Wan Shin
  • Man-Suk Oh

Abstract

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.

Suggested Citation

  • Yoon Young Jung & Dong Wan Shin & Man-Suk Oh, 2005. "Bayesian analysis of panel data using an MTAR model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(8), pages 841-854.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:841-854
    DOI: 10.1080/02664760500080132
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    References listed on IDEAS

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