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Nested Designs with AR Errors via MCMC

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Abstract

In this paper Markov Chain Monte Carlo algorithms(MCMC) are developed to facilitate the Bayesian analysis on nested designs when the error structure can be expressed as an autoregressive process of order one. Simulated and real data are also presented to confirm the efficiency and high accuracy of our work.

Suggested Citation

  • Alkhamisi, Mahdi, 2007. "Nested Designs with AR Errors via MCMC," CAFO Working Papers 2007:6, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
  • Handle: RePEc:hhs:vxcafo:2007_006
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    File URL: http://studieportal-elnu.lnu.se/mod/forum/discuss.php?d=379
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    Keywords

    Bayesian statistics; Metropolis-Hastings algorithm; Markov chain Monte Carlo methods; repeated measurements; autoregressive process; Gibbs sampling;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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