{smcl}
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help for {cmd:mclgen}{right: {browse "mailto:John_Hendrickx@yahoo.com":John Hendrickx}}
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{title:Stata macros for multinomial conditional logit models}
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{it:MCL} stands for
{it:{ul:M}ultinomial {ul:C}onditional {ul:L}ogit} 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.
{cmd:mclgen} restructures the data so the model can be estimated by
{help clogit}, {help mclest} estimates the model using {cmd:clogit}.
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In addition, {cmd:mclest} can estimate two special models:
{it:stereotyped ordered regression} (SOR) and Goodman's
{it: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.
{title:Syntax}
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{cmd:mclgen} {it:depvar}
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The {it:depvar} argument is required. {it:depvar} corresponds with the dependent variable in a multinomial logit model and should indicate a categorical {it:response factor} with a maximum of 12 levels.
{title:Description}
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Note that {cmd:mclgen} will {hi:modify the data}, and that the data
should be {hi:saved} before running {cmd:mclgen}.
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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
{help mclest} is to estimate special nonlinear designs, such as
stereotyped ordered regression and Goodman's row and columns model 2.
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In order to estimate an MCL model, the data must be transformed into a
{it:person/choice} file. In a {it:person/choice} file, each respondent
has a {ul:separate record} for each category of the
{it:response factor} (i.e. the dependent variable in a multinomial
logit model). The {it:reponse factor} indexes the
{it:response options} for respondents, a {it:stratifying variable}
indexes the respondents, and a {it:dichotomous dependent variable}
indicates which record corresponds with response option chosen by the
repondent.
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So for a response factor with 5 levels, the dataset is expanded 5
times. The response factor specified in {cmd:mclgen} indexes the
{it:response options} for each respondent. {cmd:mclgen} creates
{cmd:__strata} and {cmd:__didep}, the {it:stratifying variable} and
{it:dichotomous dependent variable} for use by {help clogit}
or {help mclest}.
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In {help clogit}, the {it:dichotomous dependent variable} is specified
as the dependent variable and the {it:stratifying variable} is
specified in the {cmd:strata(}{it:varname}{cmd:)} option. The main
effects of the {it: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.
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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 {help mlogit}. However, this procedure
followed here allows much more flexibility in imposing restrictions on
the response factor.
See {help mclest} for further information
Direct comments to: {browse "mailto:John_Hendrickx@yahoo.com":John Hendrickx}
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{cmd:mclest} is available at
{browse "http://ideas.uqam.ca/ideas/data/bocbocode.html":SSC-IDEAS}.
Use {help findit} {cmd:mcl} to locate the latest version.
{title:Also see}
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On-line: help for
{help mclest}, {help mlogit}, {help clogit}, {help desmat}, {help desrep},
{help xi}, {help xi3}, {help ologit}
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