now in those cases we can't use the normal chow tests we've got more parameters to estimate than we have observations, right, as a result chow developed a second test, right, from structural change where we don't need er erm to estimate essentially the regression in the sub sample which has got very few obser observations but in that chow, that chow second test is often called a test of predicted failure, right, microfit will  calculate both of those tests  and bear in mind i mean that we're spending a lot of time on er parameter constancy, we must bear in mind that parameter constancy is vitally important if we are going to make these inferences possible  be about policy making on the basis of our estimates.