{smcl} {.-} help for {cmd:mclgen}{right: {browse "mailto:John_Hendrickx@yahoo.com":John Hendrickx}} {.-} {p} {title:Stata macros for multinomial conditional logit models} {p} {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}. {p} 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} {p 8 27} {cmd:mclgen} {it:depvar} {p} 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} {p} Note that {cmd:mclgen} will {hi:modify the data}, and that the data should be {hi:saved} before running {cmd:mclgen}. {p} 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. {p} 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. {p} 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}. {p} 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. {p} 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} {p} {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} {p 0 21} On-line: help for {help mclest}, {help mlogit}, {help clogit}, {help desmat}, {help desrep}, {help xi}, {help xi3}, {help ologit} {p_end}