A new empirical approach for mitigating exploding implicit prices in mixed multinomial logit models

This paper introduces a new shifted negative log-normal distribution for the price parameter in mixed multinomial logit models. The new distribution, labeled as the mu-shifted negative log-normal distribution, has desirable properties for welfare analysis and in particular a point mass that is further away from zero than the negative log-normal distribution. This contributes to mitigating the exploding implicit prices issue commonly found when the price parameter is specified as negative log-normal and the model is in preference space. The new distribution is tested on five stated preference datasets. Comparisons are made with standard alternative approaches such as the willingness-to-pay (WTP) space approach. It is found that the mu-shifted distribution yields substantially lower mean marginal WTP estimates compared to the negative log-normal specification and similar to the values derived from models estimated in WTP-space with flexible distributions, while at the same time fitting the data as well as the negative log-normal specification.

Automated summary

This paper introduces a new distribution called the mu-shifted negative log-normal distribution for the price parameter in mixed multinomial logit models. The new distribution has desirable properties for welfare analysis and helps mitigate the exploding implicit prices issue commonly found in preference space models. The distribution is tested on five stated preference datasets and compared to alternative approaches such as the willingness-to-pay (WTP) space approach. The results show that the mu-shifted distribution yields lower mean marginal WTP estimates while fitting the data well.