Bayesian reduced rank regression in econometrics
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.
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- Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
- Engle, Robert & Granger, Clive, 2015.
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- John F. Geweke, 1994. "Bayesian comparison of econometric models," Working Papers 532, Federal Reserve Bank of Minneapolis.
- Kleibergen, Frank & van Dijk, Herman K., 1994. "On the Shape of the Likelihood/Posterior in Cointegration Models," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 514-551, August.
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- Izenman, Alan Julian, 1975. "Reduced-rank regression for the multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 5(2), pages 248-264, June.
- Gibbons, Michael R. & Ferson, Wayne, 1985. "Testing asset pricing models with changing expectations and an unobservable market portfolio," Journal of Financial Economics, Elsevier, vol. 14(2), pages 217-236, June. Full references (including those not matched with items on IDEAS)
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