GOLOGIT2: Stata module to estimate generalized logistic regression models for ordinal dependent variables
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). Other key strengths of gologit2 include options for linear constraints, alternative model parameterizations, automated model fitting, survey data (svy) estimation, alternative link functions (logit, probit, complementary log-log, log-log & cauchit), and the computation of estimated probabilities via the predict command. gologit2 works under both Stata 8.2 and Stata 9 or higher. Syntax is the same for both versions; but if you are using Stata 9 or higher, gologit2 supports several prefix commands, including by, nestreg, xi and sw. gologit2 is inspired by Vincent Fu's gologit program and is backward compatible with it but offers several additional powerful options. Also see Stata Journal, 6(1), 58-82.
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