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Jonathan James

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.

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James, Jonathan (2017): MM Algorithm for General Mixed Multinomial Logit Models (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022326.0703360694

Related Publication

James, J. (2017), Mm Algorithm For General Mixed Multinomial Logit Models, Journal of Applied Econometrics, 32(4), 841-857. https://doi.org/10.1002/jae.2532

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