Thanks to Kit Baum, a new program called -gologit2- is available on SSC .
Type -ssc describe gologit2- in Stata to find it. It's written for version 8.2.
gologit2 estimates generalized logistic regression models for ordinal
dependent variables. A major strength of gologit2 is that it can also
estimate two special cases of the generalized model: the proportional odds
model and the partial proportional odds model. Hence, gologit2 can
estimate models that are less restrictive than the proportional
odds/parallel lines models estimated by ologit (whose assumptions are often
violated) but more parsimonious and interpretable than those estimated by a
non-ordinal method, such as multinomial logistic regression (i.e. mlogit).
Other key strengths of gologit2 include options for linear constraints,
alternative model parameterizations, automated model fitting, Stata
8.2 survey data (svy) estimation, and the computation of estimated
probabilities via the predict command. gologit2 is inspired by Vincent Fu's
gologit program and is backward compatible with it but offers several
additional powerful options.
Note that there is an ancillary file, gologit2.pdf, that basically serves
as a manual/working paper on the program. I suspect you'll have more of a
fighting chance of understanding the program and the stat theory behind it
if you read this. It can also be downloaded separately at
I'll be doing a presentation on -gologit2- at the User Group meetings in
Boston next month. Comments on the program and its writeup are welcome and
appreciated.
