st: gologit2 2.1.4 now available from SSC

[Richard Williams <Richard.A.Williams.5@ND.edu>]

Thanks to Kit Baum, an updated version of -gologit2- is now available 
from SSC.  The old version computed Pseudo R^2 incorrectly when 
pweights were used.  This problem and others have been fixed and 
there have also been minor improvements in the documentation.

gologit2 is a user-written program that estimates generalized ordered 
logit models for ordinal dependent variables. The actual values taken 
on by the dependent variable are irrelevant except that larger values 
are assumed to correspond to "higher" outcomes. Up to 20 outcomes are 
allowed. gologit2 is inspired by Vincent Fu's gologit program and is 
backward compatible with it but offers several additional powerful options.

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 autofit option greatly simplifies the process of 
identifying partial proportional odds models that fit the data.

An alternative but equivalent parameterization of the model that has 
appeared in the literature is reported when the gamma option is 
selected. Other key advantages of gologit2 include support for linear 
constraints (making it possible to use gologit2 for constrained 
logistic regression), survey data estimation, and the computation of 
estimated probabilities via the predict command.

Also, if the user considers them more appropriate for their data, 
probit, complementary log-log, log-log and cauchit links can be used 
instead of logit by specifying the link option, e.g. link(l) for 
logit (the default), link(p) for probit, link(c) for complementary 
log-log, link(ll) for log-log, and link(ca) for cauchit.

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.  Stata 9's svy prefix command is NOT currently supported; use 
the svy option instead.

If you want to estimate marginal effects after gologit2, it is 
recommended that you install the most current versions of the 
user-written mfx2 and/or margeff commands, both available from 
SSC.  These commands are generally easier to use than mfx and make it 
possible to output the results using table-formatting programs like 
outreg2 and estout.

More information on the statistical theory behind gologit2 as well as 
several worked examples and a troubleshooting FAQ can be found at

http://www.nd.edu/~rwilliam/gologit2/.



-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
FAX:    (574)288-4373
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
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WWW (department):    http://www.nd.edu/~soc  
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