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Re: st: question RE -ml- and ancillary parameters


From   jpitblado@stata.com (Jeff Pitblado, StataCorp LP)
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: question RE -ml- and ancillary parameters
Date   Tue, 15 May 2007 14:25:10 -0500

Phil Schumm <pschumm@uchicago.edu> asks about using -ml- to fit a model where
the number of ancillary parameters is a function of the data:

> I am coding an estimator for a model in which the number of ancillary  
> parameters depends on the observed support of the dependent variable  
> -- very similar to an ordinal probit or logit model.  My first step  
> was to translate R code I was given which successfully estimates the  
> model into Stata (mostly into Mata); now, I would like to re- 
> implement the estimator using -ml- to explore the performance  
> differences and to access all of the -ml- related goodness (e.g.,  
> hooks for robust estimates of variance, svy capability, and  
> constrained estimation).
> 
> I have used -ml- frequently in the past, but never with a problem  
> like this.  Unfortunately, -_oprobit- and -_ologit- (the underlying  
> commands which implement -oprobit- and -ologit-) are built-in, so I  
> can't see how they handle this issue.  My first instinct would be to  
> construct an -ml model- statement programmatically based on the data  
> and then execute it.  Is this a reasonable approach, and are there  
> any practical limits to the number of ancillary parameters (i.e.,  
> equations) that can be specified this way?

Phil's first instinct is definitely a reasonable approach.

While -ologit- and -oprobit- put an upper limit on the number of outcomes,
-ml- does not put a limit on the number of equations.  The only limit is on
the length of a command line (see -help limits-).  Thus if you can type it
into a command line, Stata's -ml model- command can parse it.

The only other limiting factor I can think of for -ml- would be due to the
numerics of the model fit for a given dataset--some datasets with wide but
sparsely supported dependent variables could provide serious challenges for
convergence.

--Jeff
jpitblado@stata.com
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