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Regression Models under Competing Covariance Structures: A Bayesian Perspective

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  • Jacek Osiewalski
  • Mark F. J. Steel

Abstract

This paper develops Bayesian approaches to deal with linear elliptical regression models that differ in the covariance structure. A pretest method based on posterior model probabilities is compared with a pooling approach, and the data density is defined as a mixture of elliptical densities with weights that are unknown discrete parameters. An example with AR (1), MA (1) or uncorrelated errors is presented as an illustration of the ideas.

Suggested Citation

  • Jacek Osiewalski & Mark F. J. Steel, 1993. "Regression Models under Competing Covariance Structures: A Bayesian Perspective," Annals of Economics and Statistics, GENES, issue 32, pages 65-79.
    • Handle: RePEc:adr:anecst:y:1993:i:32:p:65-79
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    File URL: http://www.jstor.org/stable/20075926
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    Cited by:

    1. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    2. Carmen Fernández & Eduardo Ley & Mack F. J. Steel, "undated". "Statistical modeling of fishing activities in the North Atlantic," Working Papers 97-25, FEDEA.

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