Hello fellow Statalisters -
I am interested in estimating an ordered probit (oprobit) using
nonlinear constraints. In particular, I am estimating a model like the
one below:
oprobit y pop_1 pop_2 pop_3 pop_4
... and I would like to constrain each successive population parameter
has a coefficient lower than the previous so that (pop_2-pop_1)>=0,
(pop_3-pop_2)>=0, etc.
I recognize that there has been a thread (and faq) from a few years
back (http://www.stata.com/support/faqs/stat/intconst.html) explaining
how to set up interval (non-linear) constraints using ML to perform a
linear regression. However, even though the article suggests that I
could use the similar methodology to derive it for probits, I'm not
100% sure that it's directly applicable in my case and for oprobits. I
have not worked with stata's ML function before.
Does anyone have any advice? or, since I imagine that this problem
isn't too uncommon, does anyone have example code for using nonlinear
constraints of parameters with ordered probits?
Thanks in advance,
Dana
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