(Replying to PARENT post)
Note also that any distributional assumptions are really only necessary for inference (i.e., tests and confidence intervals) in finite samples (read: small samples); the central limit theorem guarantees the tests work asymptotically, so you're usually going to be fine.
Most of the attention paid to distributional assumptions in regression is wasted, and would be better spent on really thinking through the assumed moment conditions underlying the estimator.
👤knoepfle🕑7y🔼0🗨️0
(Replying to PARENT post)
> If the error vector in our regression model follows any distribution in the family of Elliptically Symmetric distributions, then any test statistic that is scale-invariant has the same null and alternative distributions as they have when the errors are normally distributed.