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MULTINOMIAL LOGIT MODEL

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.

Estimating Multionomial Logit Model Using Historical Data

Predicting Brand Choice Probabilities Using MNL Model Estimates

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