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首页> 外文期刊>Transportation Research Part B: Methodological >Accommodating multiple constraints in the multiple discrete-continuous extreme value (MDCEV) choice model
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Accommodating multiple constraints in the multiple discrete-continuous extreme value (MDCEV) choice model

机译：适应多重离散连续极值（MDCEV）选择模型中的多个约束

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摘要

Multiple-discrete continuous choice models formulated and applied in recent years consider a single linear resource constraint, which, when combined with consumer preferences, determines the optimal consumption point. However, in reality, consumers face multiple resource constraints such as those associated with time, money, and capacity. Ignoring such multiple constraints and instead using a single constraint can, and in general will, lead to poor data fit and inconsistent preference estimation, which can then have a serious negative downstream effect on forecasting and welfare/policy analysis. In this paper, we extend the multiple-discrete continuous extreme value (MDCEV) model to accommodate multiple constraints. The formulation uses a flexible and general utility function form, and is applicable to the case of complete demand systems as well as incomplete demand systems. The proposed MC-MDCEV model is applied to time-use decisions, where individuals are assumed to maximize their utility from time-use in one or more activities subject to monetary and time availability constraints. The sample for the empirical exercise is generated by combining time-use information from the 2008 American Time Use Survey and expenditure records from the 2008 US Consumer Expenditure Survey. The estimation results show that preferences can get severely mis-estimated, and the data fit can degrade substantially, when only a subset of active resource constraints is used.
机译：近年来制定和应用的多离散连续选择模型考虑了单个线性资源约束，当与消费者偏好结合时，可确定最佳消费点。但是，实际上，消费者面临多种资源约束，例如与时间，金钱和容量有关的资源约束。忽略这样的多个约束，而是使用单个约束，通常会导致不良的数据拟合和不一致的偏好估计，从而对预测和福利/政策分析产生严重的负面影响。在本文中，我们扩展了多离散连续极值（MDCEV）模型以适应多个约束。该表述使用灵活且通用的效用函数形式，适用于完全需求系统和不完全需求系统的情况。拟议的MC-MDCEV模型应用于时间使用决策，其中假定个人在受金钱和时间可用性约束的情况下，在一项或多项活动中最大限度地利用时间来发挥效用。通过将2008年美国时间使用情况调查中的时间使用信息与2008年美国消费者支出调查中的支出记录进行合并，生成了用于实证研究的样本。估计结果表明，当仅使用活动资源约束的一个子集时，偏好可能会受到严重的错误估计，并且数据拟合会大大降低。

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