Journal of Choice Modelling最新文献

英文中文

Social finance in emerging markets: Insights into Chinese individual investor preferences with broader implications 新兴市场的社会融资：对中国个人投资者偏好的洞察与更广泛的影响

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-12-05 DOI: 10.1016/j.jocm.2024.100534

Yaoming Liang , Ruiqi Chen , Senbin Zhang , Hongfu Liu , Li Han

Social finance, which integrates social impact with financial returns, is increasingly recognized as a catalyst for sustainable socio-economic development. This study presents an empirical analysis of Chinese individual investors' preferences for social finance products, using a customized choice experiment. The results reveal a strong investor preference for products with high annualized returns, short investment tenures, a focus on the healthcare sector, government backing, and flexible redemption options. By applying latent class analysis, we identify three distinct investor segments: Flexible Savers, Holistic Benefit Assessors, and Economic Return Seekers. The study also examines how demographic characteristics such as age, education, income, risk tolerance, philanthropic involvement, and awareness of social finance are associated with distinct investor segments. The findings suggest that future efforts should focus on designing social finance products that align with the diverse needs of these investor segments, thereby enhancing the appeal and effectiveness of social finance initiatives. Such tailored strategies could play a pivotal role in mobilizing private capital for social finance, maximizing its potential to drive sustainable socio-economic progress in emerging markets.

社会融资将社会影响与财务回报结合起来，越来越被认为是可持续社会经济发展的催化剂。本研究采用定制选择实验对中国个人投资者对社会金融产品的偏好进行了实证分析。结果显示，投资者强烈偏好具有高年化回报、投资期限短、关注医疗保健行业、政府支持和灵活赎回选择的产品。通过应用潜在类别分析，我们确定了三个不同的投资者群体：灵活储蓄者，整体利益评估者和经济回报寻求者。该研究还考察了年龄、教育程度、收入、风险承受能力、慈善参与和社会金融意识等人口特征如何与不同的投资者群体相关联。研究结果表明，未来的工作应侧重于设计符合这些投资者群体多样化需求的社会金融产品，从而提高社会金融举措的吸引力和有效性。这种量身定制的战略可以在动员私人资本进行社会融资方面发挥关键作用，最大限度地发挥其推动新兴市场可持续社会经济进步的潜力。

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引用次数: 0

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

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

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.

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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引用次数: 0

Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? 模型选择和框架效应：离散选择建模决策会影响损失规避估计值吗？

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-11-25 DOI: 10.1016/j.jocm.2024.100524

Ruth Quainoo , Gregory Howard , Vasundhara Gaur , Corey Lang

This paper examines whether the presence and magnitude of estimated loss aversion (LA) in a discrete choice experiment is a function of modeling choice. The experiment examined preferences for utility-scale solar energy siting based on a series of installation attributes and changes in household electric bill (the payment vehicle, which can increase or decrease relative to the status-quo). We employ multiple discrete choice modeling approaches and show that, across all models, the implications of accounting for loss aversion are qualitatively similar and match theoretical predictions. Despite this similarity, when comparing results across models we find that model choice has substantial impacts on estimated loss aversion. Specifically, different models estimate loss/gain ratios below two and in excess of six for the same data set. Thus, the consequences of framing decisions, which are an important aspect of nonmarket valuation, are not just the provenance of survey and choice experiment design but may also be heavily influenced by empirical model choice.

本文研究了离散选择实验中估计损失厌恶（LA）的存在和程度是否与建模选择有关。该实验根据一系列安装属性和家庭电费（支付工具，相对于现状可增加或减少）的变化，考察了人们对公用事业规模太阳能选址的偏好。我们采用了多种离散选择建模方法，结果表明，在所有模型中，考虑损失规避的影响在本质上是相似的，并且符合理论预测。尽管存在这种相似性，但在比较不同模型的结果时，我们发现模型选择对估计的损失规避有很大影响。具体地说，对于同一数据集，不同模型估计的损失/收益比率有的低于 2，有的超过 6。因此，作为非市场估价的一个重要方面，框架决策的后果不仅仅是调查和选择实验设计的结果，还可能在很大程度上受到经验模型选择的影响。

引用次数: 0

A consistent moment equations for binary probit models with endogenous variables using instrumental variables 利用工具变量建立具有内生变量的二元概率模型的一致矩方程

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-11-20 DOI: 10.1016/j.jocm.2024.100523

Louis de Grange , Felipe González , Matthieu Marechal , Rodrigo Troncoso

A methodology is developed for obtaining consistent moment estimators of the parameters in probit models that include both exogenous and endogenous variables. The approach is based on the use of instrumental variables in the formulation of moment conditions in order to solve a system of equations from which the consistent estimators are derived. The moment conditions also enable the correlations between the endogenous variables and the error terms to be estimated. Comparisons with uncorrected maximum likelihood and Heckman's classic two-stage method using simulated data demonstrate that the proposed method generates consistent estimators with relatively smaller mean square errors. We also apply our method to a real data case, confirming the good estimation properties of our new approach.

本文提出了一种方法，用于获得包含外生变量和内生变量的 probit 模型中参数的一致矩估计值。该方法的基础是在制定矩条件时使用工具变量，以求解方程组，并从中得出一致估计值。矩条件还能估计内生变量与误差项之间的相关性。利用模拟数据与未修正最大似然法和赫克曼经典两阶段法进行比较后发现，所提出的方法能得到均方误差相对较小的一致估计值。我们还将我们的方法应用于真实数据案例，证实了我们的新方法具有良好的估计特性。

引用次数: 0

Transformation-based flexible error structures for choice modeling 用于选择建模的基于变换的灵活误差结构

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-10-29 DOI: 10.1016/j.jocm.2024.100522

Chandra R. Bhat

In this paper, we propose a reverse Yeo-Johnson (YJ) transformation to accommodate flexible skewed and fat-tailed specifications of stochastic terms in multivariate choice models. Essentially, we specify a YJ transformation of the univariate error terms to a univariate symmetric distribution, and then tie the resulting transformed univariate symmetric terms into a convenient symmetric multivariate distribution. In this paper, we use a normal distribution for the transformed univariate symmetric terms and bring these together using a multivariate normal distribution. In this way, the original non-normal error terms become reverse YJ-transformed. The use of such a flexible parametric distribution lends additional robustness to the maximum likelihood (ML) estimator. The proposed approach can be applied to a number of different univariate and multivariate mixed modeling choice structures. In a demonstration application, in the current paper, the proposed model is applied to investigate the effect of urban living on walking frequency, considering the choice of urban living as being endogenous to walking frequency.

在本文中，我们提出了一种反向杨-约翰逊（YJ）变换，以适应多元选择模型中随机项的灵活倾斜和肥尾规格。从本质上讲，我们将单变量误差项指定为单变量对称分布的 YJ 变换，然后将变换后的单变量对称项绑定到方便的对称多变量分布中。在本文中，我们对转换后的单变量对称项使用正态分布，并使用多元正态分布将这些项结合在一起。这样，原来的非正态误差项就变成了反向 YJ 变换项。使用这种灵活的参数分布为最大似然估计法提供了额外的稳健性。所提出的方法可应用于多种不同的单变量和多变量混合建模选择结构。在本文的一个示范应用中，考虑到城市生活的选择是步行频率的内生因素，提出的模型被用于研究城市生活对步行频率的影响。

引用次数: 0

Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models 利用潜类和序数广义极值 (GEV) 模型为家庭网购和送货上门需求建模

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-10-16 DOI: 10.1016/j.jocm.2024.100521

Kaili Wang, Ya Gao, Khandker Nurul Habib

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.

过去十年间，电子商务的迅猛发展导致消费者的购物行为发生了巨大变化。本研究采用两个广义极值（GEV）族模型来研究家庭的电子购物需求。研究提出了一种模型结构，将基于序数的选择行为与选择者的潜在类别成员资格联合建模。在 OGEV 模型中引入潜类结构，解释了选择者的异质偏好群体与其序数选择结果之间的关系。此外，研究还应用了有序一般极值（OGEV）-负二项（NB）模型，以捕捉消费者店内购物需求与网上购物行为之间的相互作用。OGEV-NB 模型所继承的 RUM 原则允许对店内购物活动进行计量经济学估值，明确考虑家庭的电子购物需求。这两个模型都是利用在加拿大大多伦多地区（GTA）收集的数据集进行实证估算的。此外，还讨论了实证研究结果和行为影响。

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引用次数: 0

An assessment of the current use of hybrid choice models in environmental economics, and considerations for future applications 评估混合选择模型在环境经济学中的应用现状，并对未来的应用进行思考

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-10-16 DOI: 10.1016/j.jocm.2024.100520

Petr Mariel , Alaitz Artabe , Ulf Liebe , Jürgen Meyerhoff

This study examines the use of hybrid choice models (HCM), also referred to as integrated choice and latent variable (ICLV) models, within environmental valuation studies. The investigation is motivated by the fact that stated preference surveys in this field increasingly incorporate additional data into their modelling, particularly respondents' attitudes towards the environment in a broader context or specifically towards the environmental changes under evaluation. Key findings include the fact that sample sizes are usually too small for such complex models, that many studies use ad hoc scales as indicators of latent variables without first testing the validity and reliability of the scales, and that model results are often not compared with a benchmark model. One particularly notable finding of the simulation study is that excluding a latent variable, such as estimating Random Parameter Logit (RPL) instead of HCM, does not necessarily lead to biased willingness to pay (WTP) estimates. Therefore, if the inclusion of a latent construct is not critical to the study, we suggest opting for more traditional and robust models such as RPL or Latent Class Models (LCM). The perceived benefits of gaining a better understanding of how latent factors influence decisions come with risks associated with defining and estimating an HCM. To improve the quality of research, we provide recommendations for future applications of HCM in environmental economics.

本研究探讨了在环境估值研究中使用混合选择模型 (HCM)（也称为综合选择和潜变量模型 (ICLV)）的问题。这项研究的动机是，该领域的陈述偏好调查越来越多地将额外数据纳入其建模中，特别是受访者对更广泛环境的态度或对所评估环境变化的具体态度。主要发现包括：对于这种复杂的模型来说，样本量通常太小；许多研究使用临时量表作为潜在变量的指标，而没有首先测试量表的有效性和可靠性；模型结果通常没有与基准模型进行比较。模拟研究的一个特别值得注意的发现是，排除一个潜变量，如用随机参数对数（RPL）代替 HCM 进行估计，并不一定会导致对支付意愿（WTP）的估计出现偏差。因此，如果包含潜变量对研究并不重要，我们建议选择更传统、更稳健的模型，如 RPL 或潜类模型 (LCM)。要想更好地了解潜在因素如何影响决策，就必须对 HCM 进行定义和估算。为了提高研究质量，我们就 HCM 在环境经济学中的未来应用提出了建议。

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引用次数: 0

Too much, too little? A CBC approach accounting for screening from both sides 太多还是太少？从两方面考虑筛选的 CBC 方法

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-09-13 DOI: 10.1016/j.jocm.2024.100508

Lisa Wamhoff, Bernhard Baumgartner

Consumers are often assumed to use a two-stage decision process, screening out products in the first step and choosing among the remaining alternatives in the second step. When analyzing data from discrete choice studies, a compensatory decision strategy is usually presumed. Gilbride and Allenby (2004) introduced a method to model a decision process in a choice-based conjoint analysis combining the compensatory assumption with the two-stage decision process. Respondents first screen out alternatives that do not meet minimum requirements for attributes, followed by a choice between the remaining alternatives using the compensatory rule.

In this paper, we extend their approach by considering not only screening with a minimum threshold but also with a maximum value for every attribute. We compare this extension to the original method by Gilbride and Allenby (2004) and a single-step compensatory model. We do so on the basis of one simulation scenario as well as three empirical conjoint datasets.

The results indicate that two-sided screening is applied especially to prices. Both the original and extended models exhibit nearly identical performance. However, they outperform the one-step choice model that ignores screening in terms of fit and predictive validity.

消费者通常被假定使用两阶段决策过程，第一步筛选出产品，第二步在剩余的备选产品中做出选择。在分析离散选择研究的数据时，通常会假定一种补偿决策策略。Gilbride 和 Allenby（2004 年）在基于选择的联合分析中引入了一种决策过程建模方法，将补偿假设与两阶段决策过程相结合。在本文中，我们对他们的方法进行了扩展，不仅考虑了最低阈值的筛选，还考虑了每个属性的最大值。我们将这种扩展方法与 Gilbride 和 Allenby（2004 年）的原始方法以及单步补偿模型进行了比较。结果表明，双面筛选尤其适用于价格。原始模型和扩展模型表现出几乎相同的性能。结果表明，双面筛选尤其适用于价格，原始模型和扩展模型表现出几乎相同的性能，但它们在拟合度和预测有效性方面优于忽略筛选的一步选择模型。

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引用次数: 0

Corrigendum to “Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices” [J. Choice Model., 50 (2024) 100454] Ordinal-ResLogit：用于有序选择的可解释深度残差神经网络"[《选择模型》，50 (2024) 100454]

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-09-01 DOI: 10.1016/j.jocm.2024.100504

Kimia Kamal, Bilal Farooq

引用次数: 0

The impact of violations of expected utility theory on choices in the face of multiple risks 违反预期效用理论对面临多重风险时的选择的影响

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-08-23 DOI: 10.1016/j.jocm.2024.100511

Juan Marcos Gonzalez Sepulveda , George Van Houtven , Shelby D. Reed , Scott Webster , F. Reed Johnson

Use of preference information to infer risk tolerance has increased in recent years as a way to inform benefit-risk evaluations in regulatory and medical decision making. However, a framework for the measurement of tolerance for multiple uncertain outcomes has not been formalized when choices do not comply with expected utility theory (EUT). We developed a formal analytic framework for the measurement of preferences through choices under uncertainty with multiple risks. Based on the analytic framework, we find that violations of EUT can lead to interaction effects between uncertain outcomes, not just nonlinearities in the disutility of risks. Our framework also implies that measures of risk tolerance derived from utility, such as maximum-acceptable risk, must consider all relevant risks jointly if their effect on choices is expected to violate EUT. Somewhat reassuringly, however, we find that cross-outcome effects are expected to be negligible when the probabilities of other outcomes approach certainty. Finally, we identify a simple test that can help evaluate whether preferences for one uncertain outcome are affected by other uncertain outcomes.

近年来，越来越多地使用偏好信息来推断风险承受能力，以此作为监管和医疗决策中效益-风险评估的一种方法。然而，当选择不符合预期效用理论（EUT）时，衡量对多种不确定结果的容忍度的框架尚未正式确定。我们开发了一个正式的分析框架，用于衡量在具有多重风险的不确定性条件下通过选择做出的偏好。基于该分析框架，我们发现违反预期效用理论会导致不确定结果之间的互动效应，而不仅仅是风险效用的非线性。我们的框架还意味着，如果预期风险对选择的影响会违反 EUT，那么从效用（如最大可接受风险）中得出的风险容忍度衡量标准就必须联合考虑所有相关风险。不过，令人稍感欣慰的是，我们发现当其他结果的概率接近确定时，交叉结果的影响预计可以忽略不计。最后，我们确定了一个简单的检验方法，可以帮助评估对一种不确定结果的偏好是否会受到其他不确定结果的影响。

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