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Variable selection and model comparison in regression

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  • John F. Geweke

Abstract

In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. This paper interprets this specification as a mixed continuous-discrete prior distribution for coefficient values. It then utilizes a Gibbs sampler to construct posterior moments. It is shown how this method can incorporate sign constraints and provide posterior probabilities for all possible subsets of regressors. The methods are illustrated using some standard data sets.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Minneapolis in its series Working Papers with number 539.

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Date of creation: 1994
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Handle: RePEc:fip:fedmwp:539

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Keywords: Regression analysis;

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Cited by:
  1. GORDON, Stephen & BÉLANGER, Gilles, 1995. "Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes," Cahiers de recherche 9509, Université Laval - Département d'économique.
  2. Golan, Amos, 2001. "A simultaneous estimation and variable selection rule," Journal of Econometrics, Elsevier, vol. 101(1), pages 165-193, March.

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