TITLE
      'gologit2': module to estimate generalized logistic regression models for ordinal dependent variables

DESCRIPTION/AUTHOR(S)

gologit2 estimates generalized ordered logit models for ordinal
dependent variables. A major strength of gologit2 is that it can also
estimate three special cases of the generalized model: the proportional
odds/parallel lines model, the partial proportional odds model, and the
logistic regression 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). The svy:
prefix, as well as factor variables and post-estimation commands such as
margins, are supported. Other key strengths of gologit2 include options
for linear constraints, alternative model parameterizations, automated
model fitting, alternative link functions (logit, probit, complementary
log-log, log-log & cauchit), and the computation of estimated
probabilities via the predict command. gologit2 works under Stata 11.2
or higher. Those with older versions of Stata should use gologit29
instead. gologit2 is inspired by Vincent Fu's gologit program and is
backward compatible with both it and gologit29 but offers several
additional powerful options.

KW: logistic regression
KW: generalized logit
KW: proportional odds
KW: partial proportional odds
KW: logit
KW: probit
KW: cloglog
KW: loglog
KW: cauchit

      
Requires: Stata version 11.2
      
Author: Richard Williams, University of Notre Dame
Support: email rwilliam@ND.edu