Dear Statalisters, and -ml- experts in particular:
I am trying to write an -ml- routine and have hit a peculiar roadblock. I
am an -ml- novice, so hopefully there's an easy answer.
The likelihood function that I want to minimize has the form of, say, one
dependent variable y and 3 independent variables w, x and z. However, of
the independent variables, only x has a coefficient associated with it.
The other two variables, w and z, are needed for calculating the
likelihood but don't have coefficients.
My question - what's the best way of passing w and z to the likelihood
function?
I can think of 3 ways, but all seem pretty hacky:
1. Save "w z" as a string in a global macro called, say, MyMLvars. The
likelihood program mymlprog knows to look in $MyMLvars to find them.
2. In the ml model command, list w and z as additional endogenous
variables, e.g.,
ml model d0 mymlprog (y w z = x)
Then mymlprog can find w and z as $ML_y2 and $ML_y3.
3. The hackiest of all - list w and z as indep vars, but constrain the
coeffs to be zero:
constraint 1 w=0
constraint 2 z=0
ml model d0 mymlprog (y = x w z)
Is there a better way? Or, if there isn't, which of these is to be
preferred?
-Mark
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