Yow this is what makes me wonder if I can run statistics. If professional people get it wrong what chance to amateurs like me have.

"So what if I told you … panel data, or data with two dimensions, such as repeated observations on multiple cases over time, is really not that complicated. In fact, there are only two basic ways to analyze panel data, which I will explain briefly in this piece, just as every panel dataset has two basic dimensions (cases and time). However, when we confuse these dimensions, bad things can happen. In fact, one of the most popular panel data models, the two-way fixed effects model–widely used in the social sciences–is in fact statistical nonsense because it does not clearly distinguish between these two dimensions. This statement should sound implausible to you–really?, but it’s quite easy to demonstrate, as I’ll show you in this post."

[A little later]

In short:

**there are an untold number of analyses of panel data affected by an issue that is almost impossible to identify because R and Stata obscure the problem.** Thanks to multi-collinearity checks that automatically drop predictors in regression models, a two-way fixed effects model can produce sensible-looking results that are not just irrelevant to the question at hand, but practically nonsense. Instead, we would all be better served by using simpler 1-way fixed effects models (intercepts on time points or cases/subjects, but not both).

What Panel Data Is Really All About | Robert Kubinec
Notice he did not say SAS messes it up, probably because he never runs SAS.