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help for mclgen                                                  John Hendrickx
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Stata macros for multinomial conditional logit models

MCL stands for Multinomial Conditional Logit model. A conditional logit program
is used to estimate a multinomial logistic model. This produces the same
coefficients and standard errors as a regular multinomial logit program but has
the advantage that it provides great flexibility for imposing constraints on
the dependent variable.  mclgen restructures the data so the model can be
estimated by clogit, mclest estimates the model using clogit.

In addition, mclest can estimate two special models:  stereotyped ordered
regression (SOR) and Goodman's row and columns model 2 (RC2). Both models
estimate a scaling metric for the dependent variable; the RC2 model estimates a
scaling metric for a categorical independent variable as well.

Syntax

mclgen depvar

The depvar argument is required. depvar corresponds with the dependent variable
in a multinomial logit model and should indicate a categorical response factor
with a maximum of 12 levels.

Description

Note that mclgen will modify the data, and that the data should be saved before
running mclgen.

An MCL model uses a conditional logit model to estimate a multinomial logistic
model. This provides great flexibility for imposing constraints on the response
factor, the dependent variable in a multinomial logistic model. Different
constraints can be imposed on the response factor for each independent (dummy)
variable. One application is to specify loglinear models for square tables such
as quasi-independence, uniform association, symmetric association, into a
multinomial logistic model. A further extension provided by mclest is to
estimate special nonlinear designs, such as stereotyped ordered regression and
Goodman's row and columns model 2.

In order to estimate an MCL model, the data must be transformed into a
person/choice file. In a person/choice file, each respondent has a separate
record for each category of the response factor (i.e. the dependent variable in
a multinomial logit model). The reponse factor indexes the response options for
respondents, a stratifying variable indexes the respondents, and a dichotomous
dependent variable indicates which record corresponds with response option
chosen by the repondent.

So for a response factor with 5 levels, the dataset is expanded 5 times. The
response factor specified in mclgen indexes the response options for each
respondent. mclgen creates __strata and __didep, the stratifying variable and
dichotomous dependent variable for use by clogit or mclest.

In clogit, the dichotomous dependent variable is specified as the dependent
variable and the stratifying variable is specified in the strata(varname)
option. The main effects of the response factor correspond with the intercept
of a multinomial logistic model. Interactions of the response factor with
independent variables correspond with the effects of these independent
variables.

If the response factor is modelled using a fixed reference category, the log
likelihood, estimates and standard errors will be exactly the same as a model
estimated with mlogit. However, this procedure followed here allows much more
flexibility in imposing restrictions on the response factor.

See mclest for further information