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.