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| The '''`ologit`''' command fits a ordered logit model. | The '''`ologit`''' command fits an ordered logit model. |
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| The key is to recognize whether `ologit` or [[Stata/Mlogit|mlogit]] are more appropriate. Even when there is a natural ordering to the categories, `ologit` may not be a superior model. See As an example, adapted from [[https://www.statalist.org/forums/forum/general-stata-discussion/general/1653984-ordinal-or-multinomial-regression?p=1654012#post1654012]]: | The key is to recognize whether `ologit` or [[Stata/Mlogit|mlogit]] is more appropriate. Even when there is a natural ordering to the categories, `ologit` may not be a superior model. |
Stata ologit
The ologit command fits an ordered logit model.
Contents
Usage
In terms of syntax and reading output, ologit is very similar to logit. The most apparent difference is the inclusion of cuts.
. use https://www3.nd.edu/~rwilliam/statafiles/mroz.dta
. gen lfstatus = cond(hours==0, 0, cond(inrange(hours,1,1249), 1, 2))
. label define lfstatus 0 "non-participation" 1 "part-time work" 2 "full-time work"
. label values lfstatus lfstatus
. ologit lfstatus kidslt6 kidsge6 age educ exper nwifeinc
Iteration 0: log likelihood = -809.85106
Iteration 1: log likelihood = -686.68524
Iteration 2: log likelihood = -685.50088
Iteration 3: log likelihood = -685.49686
Iteration 4: log likelihood = -685.49686
Ordered logistic regression Number of obs = 753
LR chi2(6) = 248.71
Prob > chi2 = 0.0000
Log likelihood = -685.49686 Pseudo R2 = 0.1536
------------------------------------------------------------------------------
lfstatus | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
kidslt6 | -1.390614 .1813509 -7.67 0.000 -1.746055 -1.035172
kidsge6 | -.0341089 .0623172 -0.55 0.584 -.1562484 .0880307
age | -.0916151 .0121459 -7.54 0.000 -.1154207 -.0678096
educ | .158401 .0356408 4.44 0.000 .0885464 .2282557
exper | .1159833 .0112204 10.34 0.000 .0939917 .137975
nwifeinc | -.0153582 .0073408 -2.09 0.036 -.0297459 -.0009705
-------------+----------------------------------------------------------------
/cut1 | -1.75244 .7084357 -3.140949 -.3639319
/cut2 | -.3338748 .7054785 -1.716587 1.048838
------------------------------------------------------------------------------The key is to recognize whether ologit or mlogit is more appropriate. Even when there is a natural ordering to the categories, ologit may not be a superior model.
See also
Stata manual for ologit post-estimation