An MM Algorithm for Estimating the MNL Model with Product Features

8 Pages Posted: 30 Jan 2021

See all articles by Srikanth Jagabathula

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences

Ashwin Venkataraman

Jindal School of Management, UT Dallas

Date Written: November 19, 2020

Abstract

The multinomial logit (MNL) model is a workhorse model for modeling customer demand in many fields including operations, econometrics and marketing. In this work, we present a fast algorithm for solving the likelihood maximization problem for the MNL model with product features. Our algorithm falls under the general framework of minorize-maximize (MM) procedures and we show that it results in an efficient iterative procedure with closed-form updates. We establish a necessary and sufficient condition under which the optimization problem has a unique and bounded solution and establish convergence of our proposed algorithm to the global optimal solution.

Keywords: Multinomial Logit, MM Algorithm, Product Features, Maximum Likelihood

Suggested Citation

Jagabathula, Srikanth and Venkataraman, Ashwin, An MM Algorithm for Estimating the MNL Model with Product Features (November 19, 2020). Available at SSRN: https://ssrn.com/abstract=3733971 or http://dx.doi.org/10.2139/ssrn.3733971

Srikanth Jagabathula

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

Ashwin Venkataraman (Contact Author)

Jindal School of Management, UT Dallas ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

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