I am running a restricted regression with constraints on Eviews 12- The unrestricted regression has signs in the wrong direction so my understanding was that I needed to restrict it to point the regression toward the local minimum where the signs would be in the right directions. It looks like the code below (I've simplified a bit to keep it generic).
Code: Select all
PARAM C(1) = 0
PARAM C(2) = 0
PARAM C(3) = 0
PARAM C(4) = 0
equation y.ls y = C(1)*X1 + C(2)*X2 + (1-C(1)-C(2))*X3 + @LOGIT(C(3))*X4 + C(4)*X5
This works fine, but if I try and constrain C(4) or set the wrong starting values for the parameters I will get the error: "WARNING: Singular covariance - coefficients are not unique" with NA's in place of the std. errors, t-stats, and p-values.
I assume this is because I've somehow over-restricted the model or put the coefficients in the wrong starting place to solve, and that when it tries to solve the Marquardt steps somehow go in the wrong direction and cannot minimise the squares. I've tried running with the different solving algorithms using the (opstep = arg) option but got similar or worse results.
My question is is possible for me to further restrict the model without running into this error? Or will I just have to not restrict the variable/remove it? And is it somehow possible for me to see where the different minima may lie so I can set starting values to fall within those minima? The only thing I can think of for this is to create a for loop that iterates over different starting values and manually check when it stops working, but this feels a little like overkill!
Apologies in advance, as I can't post any data or actual code due to it's proprietary nature so I've had to keep it generic.
Many thanks,
MrC