The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including model uncertainty is described. The needed tools, relying on various Markov chain Monte Carlo techniques, are developed and direct sampling, with and without effect selection, is illustrated.
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Length: 23 pages Date of creation: 17 Sep 2004 Date of revision:
16 Feb 2005 Handle: RePEc:hhs:hastef:0565
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Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
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