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Nested Designs with AR Errors via MCMC

Mahdi Alkhamisi
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Mahdi Alkhamisi: Centre for Labour Market Policy Research (CAFO), Postal: Centre for Labour Market Policy Research (CAFO), Dept of Economics and Statistics, School of Management and Economics, Växjö University , SE 351 95 Växjö, Sweden

No 2007:6, CAFO Working Papers from Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics

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.

Keywords: Bayesian statistics; Metropolis-Hastings algorithm; Markov chain Monte Carlo methods; repeated measurements; autoregressive process; Gibbs sampling (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2007-10-01
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