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Practical and empirical identifiability of hybrid discrete choice models

机译：混合离散选择模型的实践和经验可识别性

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The formulation of hybrid discrete choice (HDC) models including both observable alternative attributes and latent variables associated with attitudes and perceptions has become a renewed topic of discussion in recent years. Even though there have been developments related to HDC model estimation and theoretical parameter identification, many practical and empirical issues related with HDC modelling have not been treated yet. In particular, it is known that as the HDC model estimates are not unique, it is necessary to impose some constraints on the model estimation process. In this paper we analyse the impact of different normalization approaches on parameter recovery in a simulated environment, identifying their advantages and disadvantages; we also analyse the impact of data variability on parameter recovery. We found serious problems when arbitrary values are used for normalization and when data variability is low, especially regarding the generation of the latent variables. The discrete choice model component appears to be more robust to these issues. Regarding parameter normalization, we recommend to normalize the variances associated with the HDC model's structural equations instead of the parameters of its measurement equations, as it is done more often in practice.
机译：混合离散选择（HDC）模型的建立，包括可观察到的替代属性以及与态度和感知相关的潜在变量，已成为近年来讨论的新话题。尽管已经有了与HDC模型估计和理论参数识别有关的发展，但是与HDC建模有关的许多实践和经验问题尚未得到解决。特别地，众所周知，由于HDC模型估计不是唯一的，因此有必要对模型估计过程施加一些约束。在本文中，我们分析了不同标准化方法对模拟环境中参数恢复的影响，确定了它们的优缺点；我们还分析了数据可变性对参数恢复的影响。当使用任意值进行归一化并且数据可变性较低时，尤其是在潜在变量的生成方面，我们发现了严重的问题。离散选择模型组件似乎对这些问题更健壮。关于参数归一化，我们建议将与HDC模型的结构方程式相关的方差归一化，而不是对其测量方程式的参数进行归一化，因为在实践中更经常这样做。

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