A new empirical approach for mitigating exploding implicit prices in mixed multinomial logit models
提出一种新的价格参数分布（μ-偏移负对数正态分布），用于混合多项Logit模型，能有效缓解隐含价格估计值过大的问题，并在五个数据集上验证其效果与WTP空间方法相当。
Abstract 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 μ‐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 μ‐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.