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Re: st: oprobit with cutpoints as function of covariates
At 05:33 PM 4/10/2007, [email protected] wrote:
Assuming that sigma is one for easy notation, instead of estimating
the standard
probability for outcome j given by:
(1) Pr(y=j) = Phi(c_j-xb) - Phi(c_{j-1}-xb)
I want to model c_j=f_j(z), where z is a subset of X:
(2) Pr(y=j) = Phi(f(z)-xb) - Phi(f_{j-1}(z)-xb)
where f(z) could be modelled, for example, as: c_j=c_{j-1}+b*z, so the cut
points are funtions of z and have as contant the previous one. I almost sure I
cannot do that with the command oprobit, so I've been trying to use goprobit
and gllamm. However, I am not 100% confident that using either of these two
commands would do what I want to. My questions are:
a) Can I use glamm or goprobit to estimate equation (2)?
b) Is it any possibility that changing the ado file of oprobit could estimate
equation (2) or shall I program a new Maximum likelihood? In this case, does
anybody have any ML program for oprobit in which I can base to extent it?
c) Did anybody come across to same problem?
I'm having a hard time visualizing exactly what you want, and
why. Perhaps a hypothetical example, or a substantive explanation of
when you would want to do this, would help.
Both goprobit, and gologit2 with the -link(probit)- option, support
the -constraints- option. So, if you can figure out how to specify
what you want as a linear constraint, you might be able to do
it. Here is a simple example:
. use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";
(77 & 89 General Social Survey)
. constraint 1 [#1]_cons = 1
. constraint 2 [#2]_cons = .5
. constraint 3 [#3]_cons=-.5
. gologit2 warm yr89 male , link(p) c(1-3)
Generalized Ordered Probit Estimates Number of obs = 2293
Wald chi2(6) = 428.35
Prob > chi2 = 0.0000
Log likelihood = -2962.2844 Pseudo R2 = 0.0112
( 1) [1SD]_cons = 1
( 2) [2D]_cons = .5
( 3) [3A]_cons = -.5
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1SD |
yr89 | .5751465 .0670129 8.58 0.000 .4438037 .7064893
male | -.1119678 .0501594 -2.23 0.026 -.2102784 -.0136571
_cons | 1 . . . . .
-------------+----------------------------------------------------------------
2D |
yr89 | .1486589 .0469871 3.16 0.002 .0565659 .240752
male | -.6175847 .0421769 -14.64 0.000 -.7002498 -.5349195
_cons | .5 . . . . .
-------------+----------------------------------------------------------------
3A |
yr89 | .0206849 .0514594 0.40 0.688 -.0801737 .1215436
male | -.754997 .0561626 -13.44 0.000 -.8650736 -.6449203
_cons | -.5 . . . . .
------------------------------------------------------------------------------
Alternatively, I wonder if you want to selectively free some
variables from the parallel lines constraint and then place
constraints on their estimated coefficients. Again, a hypothetical
example might help. Or, if there has been some published work that
has done this, you could provide a citation.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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