When you have data with heavy tails, doing a regression with student-t errors seems like an intuitive thing to do. While exploring this possibility, I ran into this paper:

Breusch, T. S., Robertson, J. C., & Welsh, A. H. (November 01, 1997). The emperor's new clothes: a critique of the multivariate t regression model. Statistica Neerlandica, 51, 3.) (link, pdf)

Which argues that the scale parameter and the degrees of freedom parameter are not identifiable with respect to each other in some sense and that because of this doing a regression with t errors doesn't do anything beyond what a standard linear regression does.

Zellner (1976) proposed a regression model in which the data vector (or the error vector) is represented as a realization from the multivariate Student t distribution. This model has attracted considerable attention because it seems to broaden the usual Gaussian assumption to allow for heavier-tailed error distributions. A number of results in the literature indicate that the standard inference procedures for the Gaussian model remain appropriate under the broader distributional assumption, leading to claims of robustness of the standard methods. We show that, although mathematically the two models are different, for purposes of statistical inference they are indistinguishable. The empirical implications of the multivariate t model are precisely the same as those of the Gaussian model. Hence the suggestion of a broader distributional representation of the data is spurious, and the claims of robustness are misleading. These conclusions are reached from both frequentist and Bayesian perspectives.

This surprises me.

I don't have the mathematical sophistication to evaluate their arguments well, so I have a couple of questions: Is it true that doing regressions with t-errors is not generally useful? If they are sometimes useful, have I missunderstood the paper or is it misleading? If they are not useful, is this a well known fact? Are there other ways to account for data with heavy tails?