Conditional posteriors for the reduced rank regression model
AbstractThe 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.
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Bibliographic InfoPaper provided by Örebro University, School of Business in its series Working Papers with number 2012:11.
Length: 7 pages
Date of creation: 27 May 2012
Date of revision:
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Gibbs sampling; full conditional posterior;
Find related papers by 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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- Geweke, John, 1996.
"Bayesian reduced rank regression in econometrics,"
Journal of Econometrics,
Elsevier, vol. 75(1), pages 121-146, November.
- 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.
- Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
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