### Abstract

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.

Original language | Dutch |
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Place of Publication | Tilburg |

Publisher | Econometrics |

Number of pages | 28 |

Volume | 1997-08 |

Publication status | Published - 1997 |

### Publication series

Name | CentER Discussion Paper |
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Volume | 1997-08 |

### Keywords

- Bayesian inference
- Coarse data
- Continuous distribution
- Maximum likelihood
- Missing data
- Scale mixture of Normals

## Cite this

Fernández, C., & Steel, M. F. J. (1997).

*Multivariate Student -t Regression Models: Pitfalls and Inference*. (CentER Discussion Paper; Vol. 1997-08). Econometrics.