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首页> 外文期刊>Transportation Research Part B: Methodological >A multiple discrete extreme value choice model with grouped consumption data and unobserved budgets
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A multiple discrete extreme value choice model with grouped consumption data and unobserved budgets

机译：具有分组消费数据和未观察预算的多个离散极值选择模型

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In this paper, we propose, for the first time, a closed-form multiple discrete-grouped ex-treme value model that accommodates grouped observations on consumptions rather than continuous consumptions. For example, in a time-use context, respondents tend to report their activity durations in bins of time (for example, 15-minute intervals or 30-minute intervals, depending on the duration of an activity). Or when reporting annual mileages driven for each vehicle owned by a household, it is unlikely that households will be able to provide an accurate continuous mileage value, and so it is not uncommon to solicit mileages in grouped categories such as 0-4,999 miles, 5000-9,999 miles, 10,000-14,999 miles, and so on. Similarly, when reporting expenditures on different types of commodities/services, individuals may round up or down to a convenient dollar value of multiples of 10 or 100 (depending on the length of time in which expenditures are sought). In some other cases, a product itself may be available only in specific package sizes (such as say, instant coffee, which is typically packaged in fixed sizes). In this paper, we use the so-called linear outside good utility MDCEV structure of Bhat (2018) to show how the model can be used for grouped consumption observations. Of course, this is also possible because the linear outside good utility does not need a continuous budget value, and allows for unobserved budgets. We discuss an important identification issue associated with this linear outside good utility model, and proceed to demonstrate applications of the proposed model to the case of weekend time-use choices of individuals and vehicle type/use choices of households. (C) 2020 Elsevier Ltd. All rights reserved.
机译：在本文中，我们第一次提出封闭式多个分组的ex-Trem值值模型，该价值模型适应对消费的分组观察而不是连续消费。例如，在时间使用的上下文中，受访者倾向于在时间区间报告它们的活动持续时间（例如，15分钟间隔或30分钟的间隔，这取决于活动的持续时间）。或者在为家庭拥有的每辆车推动的报告年度里程时，家庭不太可能能够提供准确的持续里程价值，因此在分组的类别中征求内部的行程并不罕见，如0-4,999英里，5000 -9999英里，10,000-14,999英里等。同样，在报告不同类型的商品/服务的支出时，个人可以向上或向下舍入到10或100的倍数方面（取决于所寻求支出的时间长度）。在一些其他情况下，产品本身可以仅在特定封装尺寸（例如，速溶咖啡，其通常以固定尺寸包装）。在本文中，我们使用所谓的线性外面的Bhat（2018）的良好实用程序MDCEV结构，以显示模型如何用于分组的消耗观察。当然，这也是可能的，因为良好的良好实用程序的线性不需要连续预算值，并允许未观察到的预算。我们讨论了与良好实用新型外部的线性相关的重要识别问题，并继续向拟议的模型展示拟议的模型的应用程序的个人和车辆类型/使用家庭使用选择的案例。 （c）2020 elestvier有限公司保留所有权利。

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