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Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University CAFO Working Papers, Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University

No 2007:6:
Nested Designs with AR Errors via MCMC

Mahdi Alkhamisi

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; (follow links to similar papers)

JEL-Codes: C11; (follow links to similar papers)

13 pages, October 1, 2007

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