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Gibbs Samplers for a Set of Seemingly Unrelated Regressions

Author

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  • W.E. Griffiths
  • Ma. Rebecca Valenzuela

Abstract

Bayesian estimation of a collection of seemingly unrelated regressions, referred to as a ‘set of seemingly unrelated regressions’ is considered. The collection of seemingly unrelated regressions is linked by common coefficients and/or a common error covariance matrix. Gibbs samplers useful for estimating posterior quantities are described and applied to two examples – a set of linear expenditure functions and a cost function and share equations from production theory.

Suggested Citation

  • W.E. Griffiths & Ma. Rebecca Valenzuela, 2004. "Gibbs Samplers for a Set of Seemingly Unrelated Regressions," Department of Economics - Working Papers Series 912, The University of Melbourne.
    • Handle: RePEc:mlb:wpaper:912
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    File URL: http://www.economics.unimelb.edu.au/downloads/wpapers-04/912.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.

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