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Parametric covariance matrix modeling in Bayesian panel regression

Author

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  • Salabasis, Mickael

    (UC AB)

Abstract

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.

Suggested Citation

  • Salabasis, Mickael, 2004. "Parametric covariance matrix modeling in Bayesian panel regression," SSE/EFI Working Paper Series in Economics and Finance 565, Stockholm School of Economics, revised 16 Feb 2005.
    • Handle: RePEc:hhs:hastef:0565
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    References listed on IDEAS

    as
    1. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    2. Boozer, Michael A., 1997. "Econometric Analysis of Panel DataBadi H. Baltagi Wiley, 1995," Econometric Theory, Cambridge University Press, vol. 13(5), pages 747-754, October.
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    More about this item

    Keywords

    Bayesian panel regression; parametric covariance; model selection;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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