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Re: st: Ordered Logit - Interact cut points
At 01:42 PM 3/18/2009, Jason Dean, Mr wrote:
Hi there, I am wondering if there is a way to interact the cut
points within the ologit command.
I am doing analysis between immigrant and native-born workers - when
I run separate ologit's the cut points are much different between groups.
However, I need to calculate the standard errors of the difference
in predicted probabilities.
If I could combine both groups into one equation and interact all
variables this would be easy as I could just use the nlcom to
calculate the difference and standard errors.
Is there a way to use nlcom with two separately run ologit regressions?
To put the problem another way: In OLS regression, there are two
ways we could estimate separate models for 2 groups. We could either
run separate regressions,
reg y x if grp == 0
reg y x if grp == 1
Or we could run a single model with interaction terms, e.g.
gen grpx = grp * x
reg y x grp grpx
Alas, that doesn't work with ologit. In OLS, the above allows the
intercepts to differ, but in ologit you still have the problem that
the cut points are constrained to be the same across groups.
I believe you can get around that by using gologit2, available from
SSC. Try something like
. use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
(77 & 89 General Social Survey)
. gen yr89male = yr89 * male
. gologit2 warm yr89 if male == 0, pl
Generalized Ordered Logit Estimates Number of obs = 1227
Wald chi2(1) = 23.92
Prob > chi2 = 0.0000
Log likelihood = -1597.2201 Pseudo R2 = 0.0075
( 1) [1SD]yr89 - [2D]yr89 = 0
( 2) [2D]yr89 - [3A]yr89 = 0
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1SD |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230983
_cons | 1.85993 .0981139 18.96 0.000 1.66763 2.05223
-------------+----------------------------------------------------------------
2D |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230983
_cons | .2867994 .0736006 3.90 0.000 .1425448 .431054
-------------+----------------------------------------------------------------
3A |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230983
_cons | -1.346833 .0829257 -16.24 0.000 -1.509364 -1.184302
------------------------------------------------------------------------------
. gologit2 warm yr89 if male == 1, pl
Generalized Ordered Logit Estimates Number of obs = 1066
Wald chi2(1) = 34.69
Prob > chi2 = 0.0000
Log likelihood = -1321.8641 Pseudo R2 = 0.0131
( 1) [1SD]yr89 - [2D]yr89 = 0
( 2) [2D]yr89 - [3A]yr89 = 0
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1SD |
yr89 | .6919058 .1174714 5.89 0.000 .4616661 .9221454
_cons | 1.521822 .0937338 16.24 0.000 1.338107 1.705537
-------------+----------------------------------------------------------------
2D |
yr89 | .6919058 .1174714 5.89 0.000 .4616661 .9221454
_cons | -.3636584 .0771198 -4.72 0.000 -.5148105 -.2125063
-------------+----------------------------------------------------------------
3A |
yr89 | .6919058 .1174714 5.89 0.000 .4616661 .9221454
_cons | -2.44437 .1148228 -21.29 0.000 -2.669419 -2.219322
------------------------------------------------------------------------------
. gologit2 warm yr89 yr89male male, npl(male) lrf
Generalized Ordered Logit Estimates Number of obs = 2293
LR chi2(5) = 153.37
Prob > chi2 = 0.0000
Log likelihood = -2919.0842 Pseudo R2 = 0.0256
( 1) [1SD]yr89 - [2D]yr89 = 0
( 2) [1SD]yr89male - [2D]yr89male = 0
( 3) [2D]yr89 - [3A]yr89 = 0
( 4) [2D]yr89male - [3A]yr89male = 0
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1SD |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230984
yr89male | .1756836 .1579257 1.11 0.266 -.1338452 .4852123
male | -.338108 .1356921 -2.49 0.013 -.6040597 -.0721563
_cons | 1.85993 .0981139 18.96 0.000 1.667631 2.05223
-------------+----------------------------------------------------------------
2D |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230984
yr89male | .1756836 .1579257 1.11 0.266 -.1338452 .4852123
male | -.6504578 .1066045 -6.10 0.000 -.8593988 -.4415168
_cons | .2867994 .0736006 3.90 0.000 .1425449 .431054
-------------+----------------------------------------------------------------
3A |
yr89 | .5162222 .105551 4.89 0.000 .3093461 .7230984
yr89male | .1756836 .1579257 1.11 0.266 -.1338452 .4852123
male | -1.097537 .1416367 -7.75 0.000 -1.37514 -.8199342
_cons | -1.346833 .0829257 -16.24 0.000 -1.509364 -1.184302
------------------------------------------------------------------------------
The first two gologit2 commands, with the pl option, give the same
results as ologit (except you have constants instead of cut
points). The 3rd command, with the npl option, allows the cutpoints
to differ by gender.
if you've never heard of gologit2 before this may all be quite
cryptic (and possibly not worth the trouble to learn if this isn't
that important to you!) But for more info you can see
http://www.nd.edu/~rwilliam/gologit2/index.html
-------------------------------------------
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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