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Variable selection and model comparison in regression

Author

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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.

Suggested Citation

  • John F. Geweke, 1994. "Variable selection and model comparison in regression," Working Papers 539, Federal Reserve Bank of Minneapolis.
  • Handle: RePEc:fip:fedmwp:539
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    File URL: http://www.minneapolisfed.org/research/common/pub_detail.cfm?pb_autonum_id=597
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    File URL: http://www.minneapolisfed.org/research/WP/WP539.pdf
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    Citations

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    Cited by:

    1. Golan, Amos, 2001. "A simultaneous estimation and variable selection rule," Journal of Econometrics, Elsevier, vol. 101(1), pages 165-193, March.
    2. Gordon, Stephen & Bélanger, Gilles, 1996. "Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes," L'Actualité Economique, Société Canadienne de Science Economique, vol. 72(1), pages 27-49, mars.
    3. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
    4. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.

    More about this item

    Keywords

    Regression analysis;

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