Multinomial Logit
(MNL) model is similar to the Binary Logit model, except that the dependent
variable is in this case will have multiple discrete outcomes, instead of just
2. Some examples are models predicting consumer choice (choose 1 out of 5
brands), models predicting market share ranks etc. The estimation technique is
very similar to the Binary Logit model, except that instead of predicting the
odds of 1 vs. 0, it predicts the odds of the different outcomes vs. a baseline
outcome. For e.g. for a model with 3 outcomes A, B, C, it estimates odds of B
vs. A and C vs. A.
Multinomial Logit models are used in applications in marketing that have
several distinct outcomes. One common application is to predict what product
or brand a customer is going to choose. Another application is to predict
purchase when the consumer may have more than two options, e.g. pay a loan
installment, pay off and close the loan or default.
MetriScient™ 
