st: oglm 1.1.2 now available from SSC

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

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

oglm estimates Ordinal Generalized Linear Models. When these models 
include equations for heteroskedasticity they are also known as 
heterogeneous choice/ location-scale / heteroskedastic ordinal 
regression models.  oglm supports multiple link functions, including 
logit (the default), probit, complementary log-log, log-log and cauchit.

When an ordinal regression model incorrectly assumes that error 
variances are the same for all cases, the standard errors are wrong 
and (unlike OLS regression) the parameter estimates are 
biased.  Heterogeneous choice/ location-scale models explicitly 
specify the determinants of heteroskedasticity in an attempt to 
correct for it.  Further, these models can be used when the 
variance/variability of underlying attitudes is itself of substantive 
interest.  Alvarez and Brehm (1995), for example, argued that 
individuals whose core values are in conflict will have a harder time 
making a decision about abortion and will hence have greater 
variability/error variances in their responses.

Several special cases of ordinal generalized linear models can also 
be estimated by oglm, including the parallel lines models of ologit 
and oprobit (where error variances are assumed to be homoskedastic), 
the heteroskedastic probit model of hetprob (where the dependent 
variable must be a dichotomy and the only link allowed is probit), 
the binomial generalized linear models of logit, probit and cloglog 
(which also assume homoskedasticity), as well as similar models that 
are not otherwise estimated by Stata.  This makes oglm particularly 
useful for testing whether constraints on a model (e.g. homoskedastic 
errors) are justified, or for determining whether one link function 
is more appropriate for the data than are others.

Other features of oglm include support for linear constraints, making 
it possible, for example, to impose and test the constraint that the 
effects of x1 and x2 are equal.  oglm works with several prefix 
commands, including by, nestreg, xi, svy and sw.  Its predict command 
includes the ability to compute estimated probabilities.  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. oglm was inspired by the SPSS PLUM routine 
but differs somewhat in its terminology, labeling of links, and the 
variables that are allowed when modeling heteroskedasticity.

More information on oglm can be found at

http://www.nd.edu/~rwilliam/oglm/index.html



-------------------------------------------
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
WWW (personal):    http://www.nd.edu/~rwilliam
WWW (department):    http://www.nd.edu/~soc 
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