Traditional multiple discrete–continuous (MDC) choice models impose tight linkages between consumers’ discrete choice and the continuous consumption decisions due to the use of a single utility parameter driving both the decision to choose and the extent of choice. Recently, Bhat (Trans Res Part B Methodol 110:261–279, 2018) proposed a flexible MDCEV model that employs a utility function with separate parameters to determine the discrete choice and continuous consumption values. However, the flexible MDCEV model assumes an independent and identically distributed (IID) error structure across the discrete and continuous baseline utilities. In this paper, we formulate a flexible non-IID multiple discrete–continuous probit (MDCP) model that employs a multivariate normal stochastic distribution to allow for a more general variance–covariance structure. In doing so, we revisit Bhat (Trans Res Part B: Methodol 109: 238-256, 2018) flexible utility functional form and highlight that the stochastic conditions he used to derive the likelihood function are not always consistent with utility maximization. We offer an alternate interpretation of the model as representing a two-step decision-making process, where the consumers first decide which goods to choose and then decide the extent of allocation to each good. We demonstrate an application of the proposed flexible MDCP model to analyze households’ expenditure patterns on their domestic tourism trips in India. Our results indicate that, if the analyst is willing to compromise on the strict utility-maximizing aspect of behavior, while also enriching the behavioral dimension through the relaxation of the tie between the discrete and continuous consumption decisions, the preferred model would be the flexible non-IID MDCP model. On the other hand, if the analyst wants the model to be strictly grounded on utility-maximizing behavior (which may also have benefits by way of welfare measure computations), and is willing to assume a very tight tie between the discrete and continuous consumption decision processes, the preferred model would be the non-IID traditional MDCP model.

传统的多重离散-连续 (MDC) 选择模型在消费者的离散选择和连续消费决策之间强加了紧密联系，因为使用单一效用参数来驱动选择决策和选择范围。最近，Bhat (Trans Res Part B Methodol 110:261–279, 2018) 提出了一种灵活的 MDCEV 模型，该模型采用具有单独参数的效用函数来确定离散选择和连续消费值。然而，灵活的 MDCEV 模型假设跨离散和连续基线实用程序的独立同分布 (IID) 误差结构。在本文中，我们制定了一个灵活的非 IID 多重离散连续概率 (MDCP) 模型，该模型采用多元正态随机分布以允许更一般的方差-协方差结构。为此，我们重新审视了 Bhat (Trans Res Part B: Methodol 109: 238-256, 2018) 灵活的效用函数形式，并强调他用来推导似然函数的随机条件并不总是与效用最大化一致。我们对该模型提供了另一种解释，它代表了一个两步决策过程，消费者首先决定选择哪些商品，然后决定对每种商品的分配程度。我们展示了所提出的灵活 MDCP 模型的应用，以分析家庭在印度国内旅游旅行中的支出模式。我们的结果表明，如果分析师愿意在行为的严格效用最大化方面做出妥协，同时还通过放松离散和连续消费决策之间的联系来丰富行为维度，首选模型将是灵活的非 IID MDCP 模型。另一方面，如果分析师希望模型严格基于效用最大化行为（这也可能通过福利措施计算带来好处），并且愿意假设离散和连续消费决策之间存在非常紧密的联系过程，首选模型将是非 IID 传统 MDCP 模型。