TY - JOUR
ID - st0208
A1 - Williams, R.
TI - Fitting heterogeneous choice models with oglm
JF - Stata Journal
PB - Stata Press
CY - College Station, TX
Y1 - 2010
VL - 10
IS - 4
SP - 540
EP - 567
KW - oglm
KW - heterogeneous choice model
KW - location-scale model
KW - gologit2
KW - ordinal regression
KW - heteroskedasticity
KW - generalized ordered logit model
UR - http://www.stata-journal.com/article.html?article=st0208
L1 - http://www.stata-journal.com/sjpdf.html?article=st0208
AB - When a binary or ordinal regression model incorrectly assumes that error
variances are the same for all cases, the standard errors are wrong and (unlike
ordinary least squares regression) the parameter estimates are biased. Heterogeneous
choice models (also known as locationâ€“scale models or heteroskedastic
ordered models) explicitly specify the determinants of heteroskedasticity in an attempt
to correct for it. Such models are also useful when the variance itself is of
substantive interest. This article illustrates how the authorâ€™s Stata program oglm
(ordinal generalized linear models) can be used to fit heterogeneous choice and
related models. It shows that two other models that have appeared in the literature
(Allisons model for group comparisons and Hauser and Andrews logistic
response model with proportionality constraints) are special cases of a heterogeneous
choice model and alternative parameterizations of it. The article further
argues that heterogeneous choice models may sometimes be an attractive alternative
to other ordinal regression models, such as the generalized ordered logit model
fit by gologit2. Finally, the article offers guidelines on how to interpret, test, and
modify heterogeneous choice models.
ER -