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Bayesian Variations on the Frisch and Waugh Theme

Author

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  • Jacek Osiewalski

    () (Cracow University of Economics)

Abstract

The paper is devoted to discussing consequences of the so-called Frisch-Waugh Theorem to posterior inference and Bayesian model comparison. We adopt a generalised normal linear regression framework and weaken its assumptions in order to cover non-normal, jointly elliptical sampling distributions, autoregressive specifications, additional nuisance parameters and multi-equation SURE or VAR models. The main result is that inference based on the original full Bayesian model can be obtained using transformed data and reduced parameter spaces, provided the prior density for scale or precision parameters is appropriately modified.

Suggested Citation

  • Jacek Osiewalski, 2011. "Bayesian Variations on the Frisch and Waugh Theme," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 3(1), pages 39-47, March.
  • Handle: RePEc:psc:journl:v:3:y:2011:i:1:p:39-47
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    File URL: http://www.cejeme.eu/publishedarticles/2011-37-21-634601002343906250-2958.pdf
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    References listed on IDEAS

    as
    1. Osiewalski, Jacek & Steel, Mark F. J., 1993. "Robust bayesian inference in elliptical regression models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 345-363.
    2. Kleibergen, Frank & Paap, Richard, 2002. "Priors, posteriors and bayes factors for a Bayesian analysis of cointegration," Journal of Econometrics, Elsevier, vol. 111(2), pages 223-249, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Bayesian inference; regression models; SURE models; VAR processes; data transformations;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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