Working Paper
Bayesian reduced rank regression in econometrics
Abstract: The reduced rank regression model arises repeatedly in theoretical and applied econometrics. To date the only general treatments of this model have been frequentist. This paper develops general methods for Bayesian inference with noninformative reference priors in this model, based on a Markov chain sampling algorithm, and procedures for obtaining predictive odds ratios for regression models with different ranks. These methods are used to obtain evidence on the number of factors in a capital asset pricing model.
Status: Published in Journal of Econometrics (Vol. 75, No. 1, November 1996, pp. 121-146)
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Bibliographic Information
Provider: Federal Reserve Bank of Minneapolis
Part of Series: Working Papers
Publication Date: 1995
Number: 540