Tools for Estimation of Grouped Conditional Logit Models

Paulo Guimaraes

North American Stata Users' Group Meetings 2006 from Stata Users Group

Abstract: In many applications of conditional logit models the choice set and the characteristics of that set are identical for groups of decision makers. In that case is possible to obtain a more computationally efficient estimation of the model by grouping the data and employing a new user-written command, "multin". The command "multin" is designed for estimation of grouped conditional logit models. It produces the same output as "clogit" but requires a more compact data layout. This is particularly relevant when the model comprises many observations and/or choices. In this situation it is possible to obtain substantial reductions in the size of the data set and the time required for estimation. I also present a tool implemented in Mata that transforms the data as required by "clogit" to the new format required by "multin". Finally, I discuss the problem of overdispersion in the grouped conditional logit model and present some alternatives to deal with this problem. One of these alternatives is Dirichlet-Multinomial (DM) regression. A new command for estimation of the DM regression model, "dirmul", is presented. The "dirmul" command can also be used to estimate the better known Beta-Binomial regression models.

Date: 2006-07-23
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Persistent link: https://EconPapers.repec.org/RePEc:boc:asug06:7

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