Kreutzer and Wood (https://www.tandfonline.com/doi/pdf/...B.82.6.357-362) showed how some undergraduate business schools "punch above their weight" in generating good outcomes for their graduates. Specifically, the authors characterized graduates' starting salaries as a function of the school's measured inputs and then analyzed the residuals of this ordinary regression estimation. Fordham's graduates, for example, had a starting salary of $52,500 -- which was $9865 above the predicted value. After regress, getting residuals is straightforward using predict .

But in my case, attempting to apply this technique, the problem is that I have a fractional outcome variable -- like "percentage of each college's law school applicants admitted to top-100 law schools." That variable necessarily lies from 0 to 1 at most. I'm trying to identify the undergraduate schools that "punch above their weight" in law school admissions and so I am looking for residuals -- what colleges have a much higher percentage than predicted? So I am using fracreg but Stata 14 does not easily generate residuals, saying

option resid not allowed.

Therefore after fracreg I try

predict yhat
generate residual = depvar - yhat

and get, apparently, the results I want, but my amateur statistician spidey-sense is tingling about these questions:

1. If it's that easy, why aren't residuals built into fracreg? I feel that option resid not allowed is warning me away from something.
2. Is that conditional mean (cm, the default), the correct counterpart of a fitted value in an Ordinary Least Squares equation?
3. Am I doing something laughably bad with this approach?