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st: Interesting (?) -ml- question


From   Mark Schaffer <M.E.Schaffer@hw.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   st: Interesting (?) -ml- question
Date   Sat, 04 Sep 2004 17:56:19 +0100 (BST)

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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