Without going to much into detail: my parameters are percentages. They
can only range from 0 to 1. There may be a better solution (i.e. a
solution that better fits the data) beyond 1, BUT, as I said, by
definition they cannot be above 1 (or below 0). So the best solution
that is possible has to be between 0 and 1.
My current model gives me values which are both below 0 (I changed the
equation a little bit, corrected a minor error, but that doesn't matter
for the problem).
If the estimated parameters are both below 0 - and 0 is the lowest value
allowable - then isn't the simplest way to impose the constraints you want
is to just drop those variables from the equation??? (That is the simplest
way I know of to constrain effects to be 0.)
