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Multivariate Student -t Regression Models: Pitfalls and Inference


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  • Fernández, C.
  • Steel, M.F.J.

    (Tilburg University, Center for Economic Research)


We consider likelihood-based inference from multivariate regression models with independent Student-t errors. Some very intruiging pitfalls of both Bayesian and classical methods on the basis of point observations are uncovered. Bayesian inference may be precluded as a consequence of the coarse nature of the data. Global maximization of the likelihood function is a vacuous exercise since the likelihood function is unbounded as we tend to the boundary of the parameter space. A Bayesian analysis on the basis of set observations is proposed and illustrated by several examples.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1997-08.

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Date of creation: 1997
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Handle: RePEc:dgr:kubcen:199708

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Related research

Keywords: Bayesian inference; Coarse data; Continuous distribution; Maximum likelihood; Missing data; Scale mixture of Normals;


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Blog mentions

As found by, the blog aggregator for Economics research:
  1. More on Student-t Regression Models
    by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2013-12-22 18:04:00
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October.
  2. David Cademartori & Cecilia Romo & Ricardo Campos & Manuel Galea, 2003. "Robust estimation of systematic risk using the t distribution in the chilean stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 10(7), pages 447-453.
  3. Manuel Galea & Heleno Bolfarine & Filidor Vilcalabra, 2002. "Influence diagnostics for the structural errors-in-variables model under the Student-t distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1191-1204.
  4. Felipe Osorio & Manuel Galea, 2006. "Detection of a change-point in student-t linear regression models," Statistical Papers, Springer, vol. 47(1), pages 31-48, January.
  5. Antonio Sanhueza & Víctor Leiva & N. Balakrishnan, 2008. "A new class of inverse Gaussian type distributions," Metrika, Springer, vol. 68(1), pages 31-49, June.
  6. Jose T.A.S. Ferreira & Mark F.J. Steel, 2004. "Bayesian Multivariate Regression Analysis with a New Class of Skewed Distributions," Econometrics 0403001, EconWPA.
  7. Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 723-740.


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