Hypothesis testing under single equation linear regression is robust to simple data normalization (for example dividing all variables by their respective mean). I see that the same is true for systems of equations estimated by SUR without any constraint. However, that is not the case when constraints are involved. Can anyone explain why? and if normalization has to be done in such cases, how should it be done?