The "Pathology" Of The Natural Conjugate Prior Density In The Regression Model
AbstractIn a Bayesian analysis of the linear regression model, one may have prior information on the error variance. If one incorporates this kind of information in a natural conjugate prior density, under certain conditions the posterior mean of the coefficients on which one is informative is equal to a constrained least squares estimator. The value of the posterior covariance matrix is also studied. We discuss and illustrate how to avoid getting posterior results too close to the âpathologicalâ results summarized above.
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Bibliographic InfoPaper provided by Universite Aix-Marseille III in its series G.R.E.Q.A.M. with number 90a14.
Length: 19 pages
Date of creation: 1990
Date of revision:
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regression analysis ; linear models ; econometrics;
Other versions of this item:
- Luc BAUWENS, 1991. "The 'pathology' of the Natural Conjugate Prior Density in the Regression Model," Annales d'Economie et de Statistique, ENSAE, issue 23, pages 49-64.
- BAUWENS, Luc, . "The "pathology" of the natural conjugate prior density in the regression model," CORE Discussion Papers RP -962, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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- Carmen Fernandez & E Ley & Mark F J Steel, 2004.
"Benchmark priors for Bayesian models averaging,"
ESE Discussion Papers
66, Edinburgh School of Economics, University of Edinburgh.
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