mm algorithm for general mixed multinomial logit models (replication data)

This paper develops a new technique for estimating mixed logit models with a simple minorization-maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asymptotically consistent, efficient and globally convergent.

Data and Resources

Suggested Citation

James, Jonathan (2017): MM Algorithm for General Mixed Multinomial Logit Models (replication data). Version: 1. Journal of Applied Econometrics. Dataset.