Tools for Estimation of Grouped Conditional Logit Models

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.