Multinomial Logit : Extending Binary Choice to Many Options

Описание: This video extends binary logit to multinomial logit, enabling models with any number of choices. We cover the mathematical structure, where iid Type I extreme value errors yield choice probabilities with exponentiated difference utilities in numerator and sum of all exponentials in denominator. The log likelihood generalizes naturally.

Slides available here: https://raw.githack.com/tyleransom/st...
Slides' source code here: https://github.com/tyleransom/structu...

Applications span transportation planning (transit ridership forecasting), marketing (product substitution), and urban economics (residential location choice). The model's elegance enables tractable estimation and counterfactual policy simulations across economics, social sciences, and business.

However, this simplicity imposes restrictions on substitution patterns—limitations we'll address next video.

Key Topics:
Multinomial logit specification and choice probabilities
Log likelihood function for J alternatives
Applications: transportation, marketing, urban economics
Substitution patterns and model predictions
Tradeoffs between elegance and flexibility

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!

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