Relaxes independence of irrelevant alternatives (IIA) assumption
Random coefficients from six distributions—normal, correlated normal, log normal, truncated normal, uniform, and triangular
Robust and cluster–robust standard errors
Support for complex survey data
Mixed logit models go by many names. A few of them are the following:
Mixed multinomial logit models
Mixed discrete choice models
Discrete choice models with random coefficients
And in earlier versions of Stata, we referred to them as alternative-specific mixed logit models.
Mixed logit models are unique among the models for choice data because they allow random coefficients.
Random coefficients are of special interest to those fitting these models because they are a way around multinomial models' IIA assumption. IIA stands for "independence of the irrelevant alternatives". If you have a choice among walking, public transportation, or a car and you choose walking, then once you have made your choice, the other alternatives should be irrelevant. If we took away one of the other alternatives, you would still choose walking, right? Maybe not. Human beings sometimes violate the IIA assumption.
Mathematically speaking, IIA makes alternatives independent after conditioning on covariates. If IIA is violated, then the alternatives would be correlated. Random coefficients allow the alternatives to be correlated.
Mixed logit models are often used in the context of random utility models and discrete choice analyses.
Stata's cmmixlogit command supports a variety of random coefficient distributions and allows for convenient inclusion of both alternative-specific and case-specific variables.
The cmxtmixlogit command fits these models for panel data.