I am trying to estimate a model of occupational choice with three choices. Are there any alternatives to using the multinomial logistic regression when handling such unordered categorical outcomes?
When dealing with binary dependent variables there seems to be several choices such as the LPM model as well as the binary probit and logit model. When dealing with unordered categorical variables the literature however keeps recommending the multinomial logit model without comparing it to alternatives.
Best Answer
There is a variety of models available to model multinomial models.
I recommend Cameron & Trivedi Microeconometrics Using Stata for an easy and excellent introduction or take a look at the Imbens & Wooldridge Lecture Slides or here which are available online.
Widely used models include:
multinomial logistic regression or mlogit in Stata
multinomial conditional logit (allows to easily include not only individual-specific but also choice-specific predictors) or asclogit in Stata
nested logit (relax the independence from irrelevant alternatives assumption (IIA) by grouping/ranking choices in an hierarchical way) or nlogit in Stata
mixed logit (relaxes the IIA assumption by assuming e.g. normal distributed parameters) or mixlogit in Stata.
multinomial probit model (can further relax the IIA assumption but you should have choice-specific predictors available) mixed logit (relaxes the IIA assumption assuming e.g. normal distributed parameters), use asmprobit in Stata (mprobit does not allow to use choice-specific predictors but you should use them to relax the IIA asumption)