Journal of Choice Modelling最新文献

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Analysis of attribute importance in multinomial logit models using Shapley values-based methods

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2025-01-31 DOI: 10.1016/j.jocm.2025.100538

Patricio Salas , Rodrigo De la Fuente , Sebastian Astroza , Juan Antonio Carrasco

This paper investigates the use of Shapley values-based methods to determine the importance of attributes in discrete choice models, specifically within a Multinomial Logit (MNL) framework. We extend the Shapley decomposition Shorrocks (2013) method from linear models. Additionally, the SHAP method Lundberg and Lee (2017) idea is applied to assess the impact of attributes on individual-level choice probability predictions. A simulation study demonstrates the effectiveness of these approaches under various experimental conditions, including attributes in several ranges and interaction terms. Finally, an empirical application is conducted using well-known travel mode choice datasets. The simulation results show that Shapley values accurately capture the global importance of attributes on goodness-of-fit. The SHAP method provides transparency in MNL model predictions, clarifying how changes in attribute values influence choice probabilities for each decision-maker. These methods offer a complementary perspective to traditional metrics like elasticities and traditional relative importance analysis Orme(2006). In the empirical application, Shapley decomposition highlights the most relevant attributes, while SHAP values uncover individual-level impacts that might not be apparent through elasticities alone. Global and individual-level analysis offers a more comprehensive understanding of attribute importance. In summary, integrating Shapley values with traditional metrics ensures a robust analysis, aiding practitioners and policymakers in making informed decisions based on broad trends and specific impacts.

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引用次数: 0

Control Function Approach for Addressing Endogeneity in Transport Models: A Case Study on the London–Amsterdam Route

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2025-01-20 DOI: 10.1016/j.jocm.2024.100537

Thomas E. Guerrero B. , Nicolò Avogadro , Raúl Ramos

Endogeneity is a key empirical challenge in transportation modeling, which may lead to inconsistent estimates and biased policy decisions. This paper investigates the sources of endogeneity and focuses on tackling this issue for a discrete choice model analyzing the multimodal London–Amsterdam route, where air transport and high-speed rail (HSR) compete. Contrary to previous literature, we found no evidence of endogeneity in service frequency for the London–Amsterdam market. This could be attributed to market-specific features, such as feeding considerations, slot retention dynamics, and the congestion of the HSR network, which constrains capacity expansion opportunities. Conversely, we observed that fare introduced endogeneity into the model. To address this issue, we applied the control function approach and proposed two novel instruments: the fare for similar markets and the price of power sources. These instruments proved to be effective in correcting for endogeneity by increasing model performance. We also discuss the adverse impact of neglecting endogeneity and estimate price and frequency elasticities, ultimately demonstrating the significance of dealing with endogeneity in ensuring the reliability of results in transportation studies and appropriately informing policy decisions.

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引用次数: 0

Context-aware Bayesian mixed multinomial logit model

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-12-12 DOI: 10.1016/j.jocm.2024.100536

Mirosława Łukawska, Anders Fjendbo Jensen, Filipe Rodrigues

Traditional choice models often entail the assumption that the preference parameters of the decision-maker are constant throughout time and across different choice situations, which may be too strong for certain choice modelling applications. This paper proposes an effective approach to model systematic, context-dependent heterogeneity, thereby introducing the concept of the context-aware Bayesian mixed multinomial logit model (C-MMNL). In this model, a neural network maps contextual information to interpretable shifts in the preference parameters of each individual in each choice occasion. The proposed model offers several key advantages. First, it supports both continuous and discrete variables, as well as complex non-linear interactions between both types of variables. Secondly, each context specification is considered jointly as a whole by the neural network, rather than each variable being considered independently. Finally, since the neural network parameters are shared across all decision-makers, it can leverage information from other decision-makers to infer the effect of a particular context on a particular decision-maker. Even though the context-aware Bayesian mixed multinomial logit model allows for flexible interactions between attributes, the increase in computational complexity is minor, compared to the mixed multinomial logit model. We illustrate the concept and interpretation of the proposed model in a simulation study. We furthermore present a real-world case study from the travel behaviour domain — a bicycle route choice model, based on a large-scale, crowdsourced dataset of GPS trajectories including 119,448 trips made by 8555 cyclists.

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引用次数: 0

New misspecification tests for multinomial logit models

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-12-12 DOI: 10.1016/j.jocm.2024.100531

Dennis Fok, Richard Paap

Multinomial Logit [MNL] models are misspecified when the Independence of Irrelevant Assumption [IIA] does not hold. In this paper we compare existing tests for IIA with two newly proposed tests. Both new tests use that, when MNL is the true model, preferences across pairs of alternatives can be described by independent binary logit models. The first test compares Composite Likelihood parameter estimates based on pairs of alternatives with standard Maximum Likelihood estimates using a Hausman (1978) test. The second is a test for overidentification in a GMM framework using more pairs than necessary. A detailed Monte Carlo study shows that the GMM test is in general superior with respect to the performance under the null and under the alternative hypothesis. An empirical illustration demonstrates the practical usefulness of the tests.

引用次数: 0

Location choice of residential housing supply: An application of the multiple discrete-continuous extreme value (MDCEV) model

IF 2.8 3区经济学 Q1 ECONOMICS

Journal of Choice Modelling

Pub Date : 2024-12-12 DOI: 10.1016/j.jocm.2024.100535

Yu Zhang, Eric J. Miller

The supply location of residential housing is the result of multiple, simultaneous decisions by housing developers. This choice situation can be characterized by the discretionary choice of locations for the housing projects and the amount of housing units to be built at the given locations. Within this context, the modelling of residential housing supply locations, or the allocation of predicted housing supply over space, is a discrete-continuous process. In this paper, we apply a multiple discrete continuous extreme value (MDCEV) model to simultaneously model the location choice and amount of housing supply. The empirical study is conducted in the city of Toronto with a pooled model, and four separated models for each structure type. The prediction results indicate reasonable fits. The developed model can be used to generate housing supply at a given period over space in an urban microsimulation system and serves as a valuable tool for policymakers, urban planners, and researchers in the field of housing supply and urban systems.

引用次数: 0

Social finance in emerging markets: Insights into Chinese individual investor preferences with broader implications

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.

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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

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Journal of Choice Modelling

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