Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2024-11-26 DOI:10.1016/j.jocm.2024.100519
Sung Hoo Kim , Patricia L. Mokhtarian
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Abstract

Latent class choice modeling has gained great popularity in the transportation and choice modeling communities across the years. However, discussion of principles associated with the specification of the class membership model has barely appeared in the literature. Related to this issue, this study questions whether one of the basic assumptions of latent class choice modeling, that of independence between latent segmentation and the behavior generation process, is tenable. We formulate latent class choice models where the unobserved influences on latent segmentation and behavior generation are correlated, by introducing an error structure reflecting that supposition. The proposed method is applied to two empirical settings. In the first application, the dependent variable is an ordinal variable measuring willingness to share autonomous vehicle rides with strangers. In the second application, the dependent variable is a binary indicator of whether a person has used ridehailing services for social purposes. In both applications, error correlations were statistically significant, indicating that the segmentation and behavior generation processes are jointly determined. Although goodness of fits and parameter estimates per se are similar to those of the standard latent class choice models for these particular applications, allowing an error structure leads to a subtle change in model implications. In particular, our scenario analyses, which present marginal effects, illustrate the value of the proposed model for considering jointness arising from correlated errors, in contrast to standard latent class models. Lastly, we propose several avenues for future research.
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具有误差结构的潜类选择模型:调查潜在细分与行为产生之间潜在的未观察关联
多年来,潜类选择建模在交通和选择建模领域大受欢迎。然而,与类成员模型规范相关的原则讨论却几乎没有出现在文献中。与此相关,本研究对潜在类别选择模型的基本假设之一,即潜在细分与行为产生过程之间的独立性是否成立提出了质疑。通过引入反映这一假设的误差结构,我们建立了潜类选择模型,其中潜细分和行为生成的未观测影响因素是相关的。我们将所提出的方法应用于两种经验设定。在第一个应用中,因变量是衡量与陌生人共享自动驾驶汽车的意愿的序变量。在第二种应用中,因变量是一个二进制指标,表示一个人是否出于社交目的使用过打车服务。在这两个应用中,误差相关性在统计上都很显著,表明细分过程和行为产生过程是共同决定的。虽然在这些特定应用中,拟合优度和参数估计本身与标准潜类选择模型相似,但允许误差结构会导致模型含义发生微妙变化。与标准潜类模型相比,我们的情景分析提出了边际效应,说明了所提模型在考虑相关误差引起的联合性方面的价值。最后,我们提出了未来研究的几个方向。
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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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