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Nested Designs with AR Errors via MCMC

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Author Info
Alkhamisi, Mahdi (Centre for Labour Market Policy Research (CAFO))
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.

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File URL: http://www.vxu.se/ehv/filer/forskning/cafo/wps/wps_nek_6_07.pdf
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Publisher Info
Paper provided by Centre for Labour Market Policy Research (CAFO), School of Management and Economics, Växjö University in its series CAFO Working Papers with number 2007:6.

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Length: 13 pages
Date of creation: 01 Oct 2007
Date of revision:
Handle: RePEc:hhs:vxcafo:2007_006

Contact details of provider:
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
Phone: +46 470 70 87 64
Web page: http://www.vxu.se/ehv/english/research/research_fields/cafo/
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Related research
Keywords: Bayesian statistics; Metropolis-Hastings algorithm; Markov chain Monte Carlo methods; repeated measurements; autoregressive process; Gibbs sampling;

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

Statistics
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This page was last updated on 2009-12-26.

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