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Conditional posteriors for the reduced rank regression model

Author

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  • Karlsson, Sune

    () (Department of Business, Economics, Statistics and Informatics)

Abstract

The multivariate reduced rank regression model plays an important role in econo- metrics. Examples include co-integration analysis and models with a factor struc- ture. Geweke (1996) provided the foundations for a Bayesian analysis of this model. Unfortunately several of the full conditional posterior distributions, which forms the basis for constructing a Gibbs sampler for the poster distribution, given by Geweke contains errors. This paper provides correct full conditional posteriors for the re- duced rank regression model under the prior distributions considered by Geweke.

Suggested Citation

  • Karlsson, Sune, 2012. "Conditional posteriors for the reduced rank regression model," Working Papers 2012:11, Örebro University, School of Business.
  • Handle: RePEc:hhs:oruesi:2012_011
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    File URL: https://www.oru.se/globalassets/oru-sv/institutioner/hh/workingpapers/workingpapers2012/wp-11-2012.pdf
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    References listed on IDEAS

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    1. John Geweke, 2004. "Getting It Right: Joint Distribution Tests of Posterior Simulators," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 799-804, January.
    2. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
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    Cited by:

    1. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, Elsevier.

    More about this item

    Keywords

    Gibbs sampling; full conditional posterior;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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