------------------------------------------------------------------------------- help formclgenJohn Hendrickx -------------------------------------------------------------------------------Stata macros for multinomial conditional logit models

MCLstands forMultinomialConditionalLogitmodel. 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.mclgenrestructures the data so the model can be estimated by clogit, mclest estimates the model usingclogit.In addition,

mclestcan estimate two special models:stereotyped orderedregression(SOR) and Goodman'srow and columns model2 (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

mclgendepvarThe

depvarargument is required.depvarcorresponds with the dependent variable in a multinomial logit model and should indicate a categoricalresponse factorwith a maximum of 12 levels.

DescriptionNote that

mclgenwillmodify the data, and that the data should besavedbefore runningmclgen.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/choicefile. In aperson/choicefile, each respondent has aseparaterecordfor each category of theresponse factor(i.e. the dependent variable in a multinomial logit model). Thereponse factorindexes theresponse optionsfor respondents, astratifying variableindexes the respondents, and adichotomousdependent variableindicates 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

mclgenindexes theresponse optionsfor each respondent.mclgencreates__strataand__didep, thestratifying variableanddichotomous dependent variablefor use by clogit or mclest.In clogit, the

dichotomous dependent variableis specified as the dependent variable and thestratifying variableis specified in thestrata(varname)option. The main effects of theresponse factorcorrespond 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

Direct comments to: John Hendrickx

mclestis available at SSC-IDEAS. Use finditmclto locate the latest version.

On-line: help for mclest, mlogit, clogit, desmat, desrep, xi, xi3, ologitAlso see